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This thesis presents work aimed at improving the accuracy of sequence-based signal peptide prediction methods by incorporating structural information. Although sequence-based methods have become increasingly accurate, even state-of-the-art predictors commonly produce false positive signal peptide predictions. Since the presence or absence of a signal peptide is crucial in determining a protein’s subcellular localization, accuracy in identifying these signals is essential. My work attempts to improve the accuracy of subcellular localization by building structure-based approaches to refine signal peptide predictions. The thesis first describes the development of a refinement method that specifically focuses on signal peptides predicted in the Escherichia coli proteome. The refinement approach follows from the observation that the region corresponding to an incorrectly predicted signal peptide generally associates with the body of the protein in the protein structure. Predicted structural information from the trRosetta web-server was incorporated in the form of inter-residue distance (contact) maps, which could be used to gauge the extent of interaction between potential signal peptides and the protein body. The classification model built based on trRosetta contact maps proved to be highly accurate in correctly classifying true false positive predicted signal peptides., Focusing next on the extension of the approach to other organisms, the thesis presents a more general approach for improving signal peptide prediction methods that takes advantage of the highly accurate protein structures predicted by AlphaFold2. It targets the most recent signal peptide prediction methods for both the prokaryotes and eukaryotes. Eventually, the method was applied to probe false positive signal peptides predicted from the proteomes of forty-eight organisms whose protein structures are directly available in the AlpahFold2 structural database., Finally, the thesis discusses the application of predicted structures of protein complexes in determining the specificity of enzymes towards predicted signal peptides. This chapter highlights several applications of the method, including the prediction of cleavage sites as well as the determination of enzyme specificity. Overall, the study demonstrates that analysis of a protein’s structure can be a powerful tool in predicting its subcellular localization.

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To overcome the cost of competition resulting from close social proximity while foraging in a group, individuals may balance their use of private (i.e. acquired from personal sampling) and social (i.e. acquired by watching other individuals) information in order to adjust their foraging strategy accordingly. Reliability of private information about environmental characteristics, such as the spatial distribution of prey, is thus likely to affect individual movement and social interactions during foraging. Our aim was to investigate how movement characteristics of foraging individuals changed as they acquired reliable private information about the spatial occurrence of prey in a foraging context. We allowed guppies (Poecilia reticulata) to develop the reliability of their private knowledge about prey spatial occurrence by repeatedly testing shoals in a foraging task under three experimental distributions of prey: 1) aggregated prey forming three patches located in fixed locations, 2) scattered distribution of prey with random locations, or 3) no prey (used as control). We then applied tracking methods to obtain individual time series of spatial coordinates from which we computed a suite of movement variables reflecting search effort, social proximity and locomotion characteristics during foraging, in order to examine changes occurring over repeated trials and to investigate which best explained foraging success. We show that foraging shoals became more efficient at finding and consuming food over the first three days by increasing their time spent active. Over time, individuals foraging on either scattered or aggregated prey travelled greater distances, showed an increasing distance to their closest neighbour and became slightly more stochastic in their acceleration profile, compared to control individuals. We found that behaviour changed as private information increased over time. Social proximity was the major predictor of foraging success in the absence of prior foraging information, while stochasticity in acceleration and search effort became the most important predictors of foraging success as information increased. In conclusion, we show that individual movement patterns changed as they acquire private information. Contrary to our predictions, the spatial distribution of prey did not affect any of the movement variables of interest. Our results emphasise the importance of information, both private and social, in shaping movement behaviour in animals. Keywords: social foraging, movement, private information, prey spatial distribution, fish.

Vuonna 2021 alkanut energiakriisi on saanut monet kotitaloudet tarkastelemaan kotinsa lämmitystekniikoita ja -tapoja. Tutkimuksemme esittelee syksyllä 2022 kerättyä laajaa energiakyselyaineistoa, jonka perusteella vain harvat suomalaiset pientalo-omistajat luottavat yhteen lämmönlähteeseen. Suurin osa (89 %) on monilämmittäjiä, jotka hyödyntävät vähintään kahta energialaitetta tai polttoainetta kotinsa lämmittämiseen, niiden välillä vaihdellen ja vuorotellen. Yleisin monilämmitysjärjestelmä on sähkölämmityksen, puulämmityksen ja ilmalämpöpumpun yhdistelmä. Tuloksemme osoittavat, että energiamurros etenee Suomessa, mutta myös sen, että päälämmityslähde-käsitteeseen nojaava virallinen lämmitystapatilastointi antaa tällä hetkellä vääristyneen kuvan suomalaisten pientalojen lämmitystodellisuudesta. Aineistomme mukaan esimerkiksi pelkällä sähköllä lämpenee vain 2 prosenttia pientaloista. Sähkölämmitystä kyllä käytetään jopa 49 prosentissa pientaloista, mutta on epäselvää, mikä rooli sähkölämmityksellä näissä kodeissa tosiasiallisesti on. Luvut näyttävät hyvin erilaisilta virallisiin lämmitystapatilastoihin verrattuna. Lämmitystapatilastojen keruussa ja esittämisessä lienee syytä siirtyä kuvailemaan talon lämmitystapojen monipuolisuutta, mikä antaisi realistisemman kuvan suomalaisten energiankäytöstä ja lämmitystavoista. Tietoa voidaan käyttää muotoilemaan vaikuttavampia politiikkatoimenpiteitä ja sähkö- tai energiatukimuotoja niille, jotka tukia todella tarvitsevat., The energy crisis that erupted made many households to review their heating techniques. Basing on an extensive energy survey data collected in 2022, only a few Finnish single-family dwellings rely on only a single heating source. The majority (89%) are multi-heaters, possessing at least two different heating solutions, between which they alternate. The most common multi-heating system is a combination of electric heating, wood heating and an air-source heat pump. Our results demonstrate that the energy transition is progressing in Finland, but also that the official heating method statistics, based on the concept of "primary heating source" gives an incomplete picture of the heating reality of Finnish small houses. According to our data, for example, only 2% of homes are heated with electricity alone. Electric heating is indeed used in up to 49% of homes, but the role of electric heating is unclear if several heating systems are installed. The figures look quite different from the official heating statistics that should be developed to reflect this versatility which would give a more realistic picture of Finns' energy use. The information can be used to formulate more effective policy measures and economic support schemes for those who really need them.

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully employed for expensive optimization. However, most SAEAs are designed for expensive unconstrained optimization, and less attention has been paid to expensive optimization with inequality constraints. Therefore, this work proposes a novel SAEA, called surrogate-assisted two-stage differential evolution (SA-TSDE), for expensive constrained optimization. In the first search stage, surrogate-assisted hybrid differential evolution is adopted for prescreening promising solutions in the decision space for exploration. Moreover, an effective repair strategy, named surrogate based repair strategy, is introduced to move the infeasible solutions closer to the feasible region. In the second search stage, a clustering strategy of feasible solutions is proposed based on the information provided by the first search stage and historical search. The clustering strategy adaptively generates a number of clusters, each of which can form a promising local region for the local search. Afterwards, local surrogates are built for finding the predicted optima in each local region. During the search process, a good balance between exploration and exploitation can be obtained by interleaving the two search stages. Experimental results indicate that SA-TSDE is highly competitive compared with some state-of-the-art methods.

Exercise group recommendation plays an important role in many intelligent education tasks. However, existing approaches make recommendations based on the intrinsic features of exercises without considering students' learning abilities, or make selections from several pre-built exercise groups at the expense of flexibility. Furthermore, although many cognitive diagnosis approaches have successfully revealed students' abilities, how to leverage the diagnosis results for exercise group recommendation is hardly explored. To flexibly recommend suitable exercise groups to students, this paper proposes to assemble personalized exercises as a group based on students' abilities, called Personalized Exercise Group Assembly (PEGA). To solve the PEGA task, we first formulate it as a constrained multi-objective problem (CMOP), where three objectives are designed for enabling the assembled exercises to enhance students' abilities on both weak and new knowledge concepts. Then, we devise an extended neural cognitive diagnosis model to learn student's ability/proficiency on all knowledge concepts to compute the weakness consolidation objective. Besides, we propose a dual-encoding and dual-population based co-evolutionary algorithm to tackle the CMOP, where the main population with binary encoding is used to search which exercises are selected, and the auxiliary population with integer encoding is responsible for accelerating the convergence of the main population via guiding offspring generation of the main population. Experiments on three popular datasets demonstrate the effectiveness of exercises assembled by the proposed algorithm compared to state-of-the-art exercise group recommendation approaches, where our assembled exercises can enhance students' proficiency on both poorly mastered and new knowledge concepts.

A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to conducting single-level analyses. The approach can also be employed as an addendum to point and interval estimation of the intraclass correlation coefficient in empirical research. The discussed procedure makes it easily possible to evaluate the design effect in two-level studies by utilizing the popular latent variable modeling methodology and is illustrated with an example.

When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor’s dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a sequence of semiparametric working models with a diverging number of predictors, which are FPCA scores with estimation errors. We show that the parametric component of the model is root-n consistent and asymptotically normal, the overall prediction error is dominated by the estimation of the nonparametric interaction function, and justify a CV-based procedure to select the tuning parameters.

For predictive quantile regressions with highly persistent regressors, a conventional test statistic suffers from a serious size distortion and its limiting distribution relies on the unknown persistence degree of predictors. This paper proposes a double-weighted approach to offer a robust inferential theory across all types of persistent regressors. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor. Under some mild conditions, it shows that the self-normalized test statistic based on the weighted estimator converges to a standard normal distribution. Our new approach enjoys a nice property that it can reach the local power under the optimal rate T with nonstationary predictor and squared root of T for stationary predictor, respectively. More importantly, our approach can be easily used to characterize mixed persistence degrees in multiple regressions. Simulations and empirical studies are provided to demonstrate the effectiveness of the newly proposed approach. The heterogenous predictability of US stock returns at different quantile levels is reexamined.

Compared with the conditional mean regression-based scalar-onfunction regression model, the scalar-on-function quantile regression is robust to outliers in the response variable. However, it is susceptible to outliers in the functional predictor (called leverage points). This is because the influence function of the regression quantiles is bounded in the response variable but unbounded in the predictor space. The leverage points may alter the eigenstructure of the predictor matrix, leading to poor estimation and prediction results. This study proposes a robust procedure to estimate the model parameters in the scalar-on-function quantile regression method and produce reliable predictions in the presence of both outliers and leverage points. The proposed method is based on a functional partial quantile regression procedure. We propose a weighted partial quantile covariance to obtain functional partial quantile components of the scalar-on-function quantile regression model. After the decomposition, the model parameters are estimated via a weighted loss function, where the robustness is obtained by iteratively reweighting the partial quantile components. The estimation and prediction performance of the proposed method is evaluated by a series of Monte-Carlo experiments and an empirical data example. The results are compared favorably with several existing methods. The method is implemented in an R package robfpqr.

Cost-effectiveness analyses of new cancer treatments in real-world settings (e.g., post-clinical trials) inform healthcare decision makers about their healthcare investments for patient populations. The results of these analyses are often, though not always, presented with statistical uncertainty. This paper identifies five ways to characterize statistical uncertainty: (1) a 95% confidence interval (CI) for the incremental cost-effectiveness ratio (ICER); (2) a 95% CI for the incremental net benefit (INB); (3) an INB by willingness-to-pay (WTP) plot; (4) a cost-effectiveness acceptability curve (CEAC); and (5) a cost-effectiveness scatterplot. It also explores their usage in 22 articles previously identified by a rapid review of real-world cost effectiveness of novel cancer treatments. Seventy-seven percent of these articles presented uncertainty results. The majority those papers (59%) used administrative data to inform their analyses while the remaining were conducted using models. Cost-effectiveness scatterplots were the most commonly used method (34.3%), with 40% indicating high levels of statistical uncertainty, suggesting the possibility of a qualitatively different result from the estimate given. Understanding the necessity for and the meaning of uncertainty in real-world cost-effectiveness analysis will strengthen knowledge translation efforts to improve patient outcomes in an efficient manner.

Gäbler, M, Prieske, O, Elferink-Gemser, MT, Hortobágyi, T, Warnke, T, and Granacher, U. Measures of physical fitness improve prediction of kayak and canoe sprint performance in young kayakers and canoeists. J Strength Cond Res XX(X): 000-000, 2021-Markers of talent selection and predictors of performance in canoe and kayak sprint are not yet well defined. We aimed to determine the combination of variables (i.e., demographic, anthropometric, and physical fitness) that most accurately predicts sprint performance (i.e., 500- and 2000-m race time) in semielite, young kayakers and canoeists (n = 39, age 13 year, 10F). The level of significance was set at p < 0.05. Linear regression analyses identified boat type (i.e., kayak or canoe), skeletal muscle mass, and average power during a 2-minute bench pull test, normalized to body mass, as predictors of 2000-m race time (R22000 m = 0.69, Akaike information criterion [AIC] = 425) and together with vertical jump height, as predictors of 500-m race time (R2500 m = 0.87, AIC = 255). This was an improvement over models containing solely demographic variables (R2500 m = 0.66, AIC = 293; R22000 m = 0.44, AIC = 446) and over models containing demographic and anthropometric variables (R2500m = 0.79, AIC = 277; R22000 m = 0.56, AIC = 437). Race time showed the strongest semipartial correlations with the 2-minute bench pull test (0.7 ≤ r ≤ 0.9). Adding physical fitness data (i.e., 2-minute bench pull test) to demographic and anthropometric data improves the prediction accuracy of race times in young kayak and canoe athletes. The characteristics of physical fitness tests should resemble as much as possible the biomechanical (e.g., prime movers) and metabolic (e.g., duration) demands of the sport.

Background Mapping disease rates is an important aspect of epidemiological research because it helps inform public health policy. Disease maps are often drawn according to local administrative areas (LAAs), such as counties, cities, or towns. In LAAs with small populations, disease rates are unstable and are prone to appear extremely high or low. The empirical Bayes methods consider variance differences among different LAAs, thereby stabilizing the disease rates. The methods of kriging break the constraints of geopolitical boundaries and produce a smooth curved surface in the form of contour lines, but the methods lack the stabilizing effect of the empirical Bayes methods. Methods An easy-to-implement stabilized kriging method is proposed to map disease rates, which allows different errors in different LAAs. Results Monte Carlo simulations revealed that the stabilized kriging method had smaller symmetric mean absolute percentage error than three other types of methods (original LAA-based method, empirical Bayes methods, and traditional kriging methods) in nearly all scenarios considered. Real-world data analysis of oral cancer incidence rates in men from Taiwan demonstrated that the age-standardized rates in the central mountainous sparsely-populated region of Taiwan were stabilized using our proposed method, with no more large differences in numerical values, whereas the rates in other populous regions were not over smoothed. Additionally, the stabilized kriging map had improved resolution and helped locate several hot and cold spots in the incidence rates of oral cancer. Conclusions We recommend the use of the stabilized kriging method for mapping disease rates.

A new Bayesian–inspired statistic for hypothesis testing is proposed which compares two posterior distributions; the observed posterior and the expected posterior under the null model. The Kullback–Leibler divergence between the two posterior distributions yields a test statistic which can be interpreted as a penalized log–Bayes factor with the penalty term converging to a constant as the sample size increases. Hence, asymptotically, the statistic behaves as a Bayes factor. Viewed as a penalized Bayes factor, this approach solves the long standing issue of using improper priors with the Bayes factor, since only posterior summaries are needed for the new statistic. Further motivation for the new statistic is a minimal move from the Bayes factor which requires no tuning nor splitting of data into training and inference, and can use improper priors. Critical regions for the test can be assessed using frequentist notions of Type I error.

Resumen (Objetivo) En esta investigación se presenta el proceso de construcción y validación del Cuestionario MTSK-estocástico. (Metodología) A través de un proceso que consta de tres fases: 1) revisión bibliográfica; 2) construcción de la versión final; 3) validación a través del juicio de expertos y la posterior aplicación piloto, se construye un cuestionario abierto para analizar y evaluar aspectos del conocimiento especializado del profesorado de Educación Primaria en activo para enseñar estadística y probabilidad, con base en el Modelo del conocimiento especializado del profesorado de Matemáticas (MTSK) y el Enfoque de los itinerarios de enseñanza de las matemáticas (EIEM). (Resultados) Las puntuaciones de las evaluaciones y opiniones expertas han proporcionado una valoración general positiva, no obstante, se eliminaron algunos ítems y se reformularon otros para mejorar su claridad y comprensión. (Conclusiones) La versión final del cuestionario, que consta de 5 ítems para el análisis de la estadística y 5 ítems para la probabilidad, permite aportar certezas sobre el dominio del conocimiento matemático: conocimiento de los temas, el conocimiento de la estructura matemática, el conocimiento de la práctica matemática; y sobre el dominio del conocimiento didáctico: el conocimiento de los estándares de aprendizaje, el conocimiento de las características de aprendizaje y, finalmente, el conocimiento de la enseñanza de las matemáticas. Abstract (Objective) This research presents the process of construction and validation of an MTSK-Stochastic Questionnaire. (Methodology) Through a process consisting of three phases - 1) bibliographic review; 2) construction of the initial version; 3) validation through expert judgment and subsequent implementation of the pilot (trial) - an open questionnaire was constructed to assist in gathering data for analysis and evaluation of aspects of specialized knowledge of in-service Primary Education teachers for teaching statistics and probability, based on the Mathematics Teacher's Specialized Knowledge (MTSK) model and the Mathematics Teaching Itinerary Approach (MTIA). (Results) The evaluations and opinions of the experts were generally positive, although some elements of the initial questionnaire were eliminated and others were reformulated to improve clarity and ease of understanding. (Conclusions) The final version of the questionnaire, which consists of 5 items for the analysis of statistics and 5 items for the evaluation of probability, makes it possible to provide accurate evaluations of mastery of mathematical knowledge: knowledge of topics, mathematical structure, and mathematical practice; and, in the didactic domain, accurate evaluations of knowledge of learning standards and characteristics, as well as knowledge about mathematics teaching. Resumo (Objetivo) Esta pesquisa apresenta o processo de construção e validação do Questionário Estocástico MTSK. (Metodologia) Mediante um processo de três fases: 1) revisão da literatura; 2) construção da versão final; 3) validação por meio de opinião profissional e subsequente aplicação piloto, foi construído um questionário aberto para analisar e avaliar aspectos do conhecimento especializado de professores ativos do Ensino Fundamental para ensinar estatística e probabilidade, com base no Modelo de Conhecimento Especializado dos professores de Matemática (MTSK) e na Abordagem dos Roteiros de Educação Matemática (EIEM, por suas siglas em espanhol). (Resultados) Os escores de avaliação e as opiniões dos especialistas forneceram uma avaliação geral positiva, no entanto, alguns itens foram removidos e outros reformulados para melhor clareza e compreensão. (Conclusões) A versão final do questionário, que consiste em 5 itens para a análise da estatística e 5 itens para a probabilidade, permite fornecer certezas sobre o domínio do conhecimento matemático: conhecimento dos temas, conhecimento da estrutura matemática, conhecimento da prática matemática; e sobre o domínio do conhecimento didático: o conhecimento das normas de aprendizagem, o conhecimento das características de aprendizagem e, finalmente, o conhecimento do ensino de matemática.

Medical devices play an essential role in healthcare. However, statistics on the Japanese medical device industry are insufficient. This study provides statistics for Japan’s medical device manufacturing industry from 1994 to 2016 using the Census of Manufacture (Ministry of Economy, Trade, and Industry). In addition, this study presents a fundamental analysis of industry, productivity analysis, and inter-industry comparison. As evaluated by labour productivity and total factor productivity, the medical device manufacturing industry (1) is research and development (R&D) intensive, (2) does not have sufficient investment in R&D, and (3) has low productivity. This study concludes that it is essential to improve the accuracy of data in the future and to publish data regularly.

The modeling of wireless communications channels is often broken down into two distinct states, defined according to the optical viewpoints of the transmitter (TX) and receiver (RX) antennas, namely line-of-sight (LoS) and non-LoS (NLoS). Movement by the TX, RX, both and/or objects in the surrounding environment means that channel conditions may transition between LoS and NLoS leading to a third state of signal propagation, namely quasi-LoS (QLoS). Unfortunately, this state is largely ignored in the analysis of signal propagation in wireless channels. We therefore propose a new statistical framework that unifies signal propagation for LoS, NLoS, and QLoS channel conditions, leading to the creation of the Three State Model (TSM). The TSM has a strong physical motivation, whereby the signal propagation mechanisms underlying each state are considered to be similar to those responsible for Rician fading. However, in the TSM, the dominant signal component, if present, can be subject to shadowing. To support the use of the TSM, we develop novel formulations for the probability density functions of the in-phase and quadrature components of the complex received signal, the received signal envelope, and the received signal phase. Additionally, we derive an expression for the complex autocorrelation function of the TSM, which will be of particular importance in understanding and simulating its time correlation properties. Finally, we show that the TSM provides a good fit to field measurements obtained for two different bodycentric wireless channels operating at 2.45 GHz, which are known to be subject to the phenomena underlying the TSM., The State Research Agency (AEI) of Spain, The European Social Fund under grant RYC2020-030536-I, AEI under grant PID2020-118139RB-I00.

The reliability of the result significantly affects the use of the Coefficient of Variation (within the test variation), which reflects the variability of the concrete properties rather than the standard deviation. For the estimation of reliability and precision, comprehensive statistical analysis (within test variation) is conducted on 534 test samples using a rebound hammer. Different standards, like BIS, EN, ACI, and ASTM, are used in this study to compare the test results. Empirical research revealed that there are contradictions in literature as well as the standards used in many countries. The present paper focuses on detailed investigations of uncertainty measurements of the surface hardness test by statistical parameters (range of rebound index, standard deviation, and Coefficient of Variation). The unbiased estimate of the variability parameters from the published results has been verified concerning to the European standard. Concrete parameters namely cement type, water to cement ratio, compaction deficiency, curing condition, and elevated temperature have an impact on the output of the rebound hardness in terms of the variability of the compressive strength. Based on the results, the authors are willing to propose amendments to the Indian code graciously in compliance with the variability of the rebound hammer.

Driving under the influence of alcohol or drugs is a very common behavior in our environment and a serious problem for public health. On the one hand, in 2016, 400,000 people died in the world in traffic accidents in which ethanol was involved. On the other hand, traffic accidents in which the use of drugs of abuse other than ethyl alcohol accounted for more than 160,000 deaths worldwide in 2017. The objective of this work is to carry out a review of the 710 cases of people who died in traffic accidents received at the forensic toxicology service of the Institute of Forensic Sciences of the University of Santiago de Compostela (Galicia-Spain) over a period of 10 years (2009–2019). We performed an observational study of period prevalence, in which the following data were collected: age, sex, year, and analytical results in plasma, in the case of being positive. The data collected was subjected to statistical treatment. Of the 710 cases analyzed, 123 correspond to pedestrians and 587 to occupants of vehicles or motorcycles. A total of 77.6% of the deceased were men. At least one psychotropic substance was found in the blood of almost 40% of the victims. The most frequently found substance was ethyl alcohol, which appeared in 231 cases, more frequently in males. The second place is occupied by benzodiazepines, which appeared in 43 cases, followed by cocaine, which was detected in 25 cases. Polydrug use was found in only 44 cases, with the association of ethanol and cocaine being the most commonly found, followed by that of ethanol and benzodiazepines. Only in 5 of the cases analyzed there were 3 or more substances present. With the data obtained in this study, it is shown that in traffic accidents, the finding of different toxic or medicinal substances is frequent. Ethyl alcohol continues to be very present in road accidents (most detected substance), with the great impact that this implies. Secondly, the presence of benzodiazepines stands out, and cocaine is the third most detected toxic in this study. These results allow to obtain a profile of the substances most frequently involved in traffic accidents. Despite the surveillance, control, and information campaigns that the Spanish Government regularly carries out, the results are far from satisfactory., Springer Nature

To prevent mistakes in psychological assessment, the precision of test norms is important. This can be achieved by drawing a large normative sample and using regression-based norming. Based on that norming method, a procedure for sample size planning to make inference on Z-scores and percentile rank scores is proposed. Sampling variance formulas for these norm statistics are derived and used to obtain the optimal design, that is, the optimal predictor distribution, for the normative sample, thereby maximizing precision of estimation. This is done under five regression models with a quantitative and a categorical predictor, differing in whether they allow for interaction and nonlinearity. Efficient robust designs are given in case of uncertainty about the regression model. Furthermore, formulas are provided to compute the normative sample size such that individuals' positions relative to the derived norms can be assessed with prespecified power and precision. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

It is highly desirable to adapt the reference vectors to unknown Pareto fronts (PFs) in decomposition-based evolutionary many-objective optimization. While adapting the reference vectors enhances the diversity of the achieved solutions, it often decelerates the convergence performance. To address this dilemma, we propose to adapt the reference vectors and the scalarizing functions in a coordinated way. On the one hand, the adaptation of the reference vectors is based on a local angle threshold, making the adaptation better tuned to the distribution of the solutions. On the other hand, the weights of the scalarizing functions are adjusted according to the local angle thresholds and the reference vectors' age, which is calculated by counting the number of generations in which one reference vector has at least one solution assigned to it. Such coordinated adaptation enables the algorithm to achieve a better balance between diversity and convergence, regardless of the shape of the PFs. Experimental studies on MaF, DTLZ, and DPF test suites demonstrate the effectiveness of the proposed algorithm in solving problems with both regular and irregular PFs.

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. In this paper, we consider statistical inference for quantile regression with large-scale data in the "increasing dimension" regime. We provide a comprehensive and in-depth analysis of a convolution-type smoothing approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as {\it{conquer}}, turns the non-differentiable quantile loss function into a twice-differentiable, convex and locally strongly convex surrogate, which admits a fast and scalable Barzilai-Borwein gradient-based algorithm to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on both estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on the number of the regressors than needed for conventional quantile regression. Moreover, we prove the validity of multiplier bootstrap confidence constructions. Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the \texttt{R} package \texttt{conquer}., An R package conquer for fitting smoothed quantile regression is available in CRAN, https://cran.r-project.org/web/packages/conquer/index.html

Berry et al. (2020) noted that predictive bias is a function of three factors: subgroup mean difference on the predictor (dx), subgroup mean difference on the criterion (dy), and test validity (rxy). They used meta-analytic estimates of each of these three to examine predictive bias against Hispanic test takers when cognitive tests are used in personnel selection. They found that tests underpredict Hispanic job performance by an average of .21 SDs, which would call into question the fairness of cognitive test use in personnel selection. We located 119 studies in which all three parameters-dy, dx, and rxy-could be obtained, thus holding sample, setting, and operationalization constant in estimating the three parameters within each study. This produced a substantially different conclusion: We find that tests overpredict Hispanic performance by .04-.20 SDs, depending on assumptions made about artifact corrections. Factors contributing to differences between the two studies include differences in range restriction corrections, sample incomparability, and Berry et al.'s use of rxy estimated from the total sample rather than within the majority subgroup. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Accurate age-depth models for marine sediment cores are crucial for understanding of paleo-oceanographic and -climatic changes derived from these archives. To date, information on bulk sediment composition is largely ignored as a potential source of information to improve age-depth models. Here, we explore how bulk sediment composition can be used qualitatively to improve age-depth models. We developed the BomDia, algorithm, which produces age-depth models with realistic sediment accumulation rates that co-vary in harmony with the bulk sediment composition. We demonstrate that changes in the marine versus terrigenous sediment deposition based on bulk sediment composition, can be used to significantly improve age-depth models of hemipelagic marine deposits. Based on two marine records - each containing more than twenty radiocarbon (AMS 14C) dated levels - we show that the mean error of prediction of unused AMS 14C ages significantly improves from 3.9% using simple linear interpolation, to 2.4% (p = 0.003) when bulk sediment composition is included. The BomDia age modelling approach provides a powerful statistical tool to assess the validity of age control points used and also may assist in the detection of hiatuses. Testing and further development of the BomDia algorithm may be needed for application in other than hemipelagic depositional settings. Key Points The accuracy and predictive quality of age-depth models for marine hemipelagic cores improves when bulk sediment composition is included The BomDia algorithm produces age-depth models with realistic accumulation rates which co-vary with bulk sediment composition The Bomdia algorithm uses a Monte Carlo approach to assess age-depth model uncertainty Plain Language Summary The age-depth relationship in a marine sediment core is known as an age-depth model. To build an age-depth model, that is to produce a continuous age-depth relationship, an accurate and precise prediction of ages in between age control points is needed. A new algorithm, which includes information on the bulk sediment composition in the prediction of ages, has been developed and tested for this purpose. The calcium carbonate content of the sediments is calibrated versus the log ratio of titanium and calcium as obtained from resolution X-ray fluorescence and used as model input for bulk sediment composition. We tested the algorithm using data from two tropical hemipelagic cores and find that the algorithm, which we refer to as BomDia (‘a good dynamic interpolation algorithm’), produces robust age-depth models with predictive power and realistic sedimentation rates that co-vary with sediment composition. Further application and testing of the algorithm to a wider variety of other than tropical hemipelagic sedimentary settings is recommended.

Like many psychological scales, depression scales are ordinal in nature. Depression prediction from behavioural signals has so far been posed either as classification or regression problems. However, these naive approaches have fundamental issues because they are not focused on ranking, unlike ordinal regression, which is the most appropriate approach. Ordinal regression to date has comparatively few methods when compared with other branches in machine learning, and its usage is limited to specific research domains. Ordinal logistic regression (OLR) is one such method, which is an extension for ordinal data of the well-known logistic regression, but is not familiar in speech processing, affective computing or depression prediction. The primary aim of this study is to investigate proportionality structures and model selection for the design of ordinal regression systems within the logistic regression framework. A new greedy based algorithm for partial proportional odds model selection (GREP) is proposed that allows the parsimonious design of effective ordinal logistic regression models, which avoids an exhaustive search and outperforms model selection using the Brant test. Evaluations on the DAIC-WOZ and AViD depression corpora show that OLR models exploiting GREP can outperform two competitive baseline systems (GSR and CNN), in terms of both RMSE and Spearman correlation.

A key factor in big data analytics and artificial intelligence is the collection of user data from a large population. However, the collection of user data comes at the price of privacy risks, not only for users but also for businesses who are vulnerable to internal and external data breaches. To address privacy issues, local differential privacy (LDP) has been proposed to enable an untrusted collector to obtain accurate statistical estimation on sensitive user data (e.g., location, health, and financial data) without actually accessing the true records. As key-value data is an extremely popular NoSQL data model, there are a few works in the literature that study LDP-based statistical estimation on key-value data. However, these works have some major limitations, including supporting small key space only, fixed key collection range, difficulty in choosing an appropriate padding length, and high communication cost. In this paper, we propose a two-phase mechanism PrivKVM* as an optimized and highly-complete solution to LDP-based key-value data collection and statistics estimation. We verify its correctness and effectiveness through rigorous theoretical analysis and extensive experimental results.

COVID-19 is a worldwide public health threat. Diagnosis by RT-PCR has been employed as the standard method to confirm viral infection. Sample pooling testing can optimize the resources by reducing the workload and reagents shortage, and be useful in laboratories and countries with limited resources. This study aims to evaluate SARS-CoV-2 detection by sample pooling testing in comparison with individual sample testing.We created 210 pools out of 245 samples, varying from 4 to 10 samples per pool, each containing a positive sample. We conducted detection of SARS-CoV-2-specific RdRp/E target sites.Pooling of three samples for SARS-CoV-2 detection might be an efficient strategy to perform without losing RT-PCR sensitivity.Considering the positivity rate in Dominican Republic and that larger sample pools have higher probabilities of obtaining false negative results, the optimal sample size to perform a pooling strategy shall be three samples.La COVID-19 es una amenaza de salud pública mundial. La RT-PCR es el método estándar para confirmar la infección. La estrategia de pruebas de muestras agrupadas puede reducir la carga de trabajo y la escasez de reactivos, y ser útil en países con escasos recursos. Evaluamos la detección del SARS-CoV-2 mediante esta estrategia en comparación con pruebas individuales.Creamos 210 grupos de 245 muestras, de 4 a 10 muestras por grupo, cada uno con una muestra positiva. Realizamos extracción de ARN y qRT-PCR para detectar la presencia de la diana RdRp/E.La combinación de hasta 3 muestras para la detección del SARS-CoV-2 podría ser una estrategia eficaz sin perder la sensibilidad.Considerando la tasa de positividad en República Dominicana y que los grupos con más muestras tienen mayor probabilidad de obtener resultados falsos negativos, el tamaño óptimo para realizar esta estrategia es de 3 muestras.

Although difficulties processing both symbolic and nonsymbolic proportion compared with absolute number are well established, the mechanisms involved remain unclear. We investigate four potential explanations to account for better number processing in adulthood: (a) number is more salient than proportion, (b) number is encoded more automatically than proportion, (c) proportion is more effortfully processed than number, and (d) number competes with proportion during decision making. Across three experiments, we used a delayed match-to-sample paradigm in which adults were asked which of two alternatives matched a sample set of red and blue dots. We systematically manipulated which dimension of the sample participants matched (number of red dots, total number of dots, proportion of red dots), the presence/absence of the competing quantity in the choice alternatives, and when they were told which quantitative dimension to encode (before vs. after the sample presentation, or not at all). Overall, data reveal that proportion was less salient than the numerical subset. Additionally, the number of items within the subset, but not the total number of items in the superset, interfered with proportion-based responding. Last, even in the absence of response competition and costly task demands, proportion matching took longer than number matching, highlighting basic processing differences. Together, results reveal pervasive difficulties in representing proportion compared with number, even when task demands are unambiguous. However, this varied depending on the numerical set involved and across encoding, processing, and decision processes. We discuss the implications of these findings for theories of ratio processing and of quantity more generally. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Since the start of COVID-19 pandemic, the definition of normal life has changed drastically. The number of cases of this pandemic is rising everyday across the globe. In this study, we propose a compartmental model, which considers the isolation factor of Coronavirus infected individuals. The model consists of five compartments: susceptible (S), exposed (E), Infected (I), Isolated (L) and recovered (R). We have estimated the parameters of the model system and the expression of the basic reproduction number \begin{document}$ R_0 $\end{document} using real data set. The exact value of the basic reproduction number is computed for India, Brazil and Peru. The local and global stability analysis of disease-free equilibrium and endemic equilibrium points is carried out. The forecasting of the pandemic is done using real data. It has been observed that to understand the pandemic the time frame has to be divided into small intervals as the parameters of the pandemic are changing with time. Within a time frame of approximately four months (i.e. from July to October 2020), the transmission rate of India has been reduced by approximately 84%. Whereas the transmission rate in Brazil and Peru has increased by 79% and 45% respectively. The sensitivity of various parameters involved in the model has been analyzed. We have presented a complete analysis to check the existence of backward bifurcation.

The Pareto front (PF) estimation has become an emerging strategy for solving multiobjective optimization problems in recent studies. By approximating the geometrical structure of the PF during the evolutionary procedure, some PF estimation approaches have been suggested and shown effectiveness in guiding the search direction of evolutionary algorithms. However, these approaches encounter difficulties in handling irregular PFs, whose geometrical structures are too complex to be properly approximated. To address this issue, this article proposes a novel PF estimation approach based on local models. In contrast to existing approaches estimating the PF via a reference point set or a single model, the proposed approach automatically divides the population into several groups and builds a local model for each group of solutions. In spite of the simplicity of each local model, the combination of all the local models can approximate the PFs with complex geometrical structures. An evolutionary algorithm is then developed based on the local model-based PF estimation approach and a novel fitness function and is compared with four evolutionary algorithms on 39 problems. Statistical results indicate that the proposed algorithm exhibits better performance than the compared algorithms, especially on problems with highly irregular PFs.

We evaluate the accuracy of model selection and associated short-run forecasts using beta autoregressive moving average ( β ARMA ) models, which are tailored for modeling and forecasting time series that assume values in the standard unit interval, ( 0 , 1 ) , such as rates, proportions, and concentration indices. Different model selection strategies are considered, including one that uses data resampling. Simulation evidence on the frequency of correct model selection favors the bootstrap-based approach. Model selection based on information criteria outperforms that based on forecasting accuracy measures. A forecasting analysis of the proportion of stored hydroelectric energy in South Brazil is presented and discussed. The empirical evidence shows that model selection based on data resampling typically leads to more accurate out-of-sample forecasts.

This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

Missing values in data impede the task of inference for population parameters of interest. Multiple Imputation (MI) is a popular method for handling missing data since it accounts for the uncertainty of missing values. Inference in MI involves combining point and variance estimates from each imputed dataset via Rubin’s rules. A sufficient condition for these rules is that the estimator is approximately (multivariate) normally distributed. However, these traditional combining rules get computationally cumbersome for multicomponent parameters of interest, and unreliable at high rates of missingness (due to an unstable variance matrix). New combining rules for univariate F- and Beta-statistics from multiply-imputed data are proposed for decisions about multicomponent parameters. The proposed combining rules have the advantage of being computationally convenient since they only involve univariate F- and Beta-statistics, while providing the same inferential reliability as the traditional multivariate combining rules. Simulation study is conducted to demonstrate that the proposed method has good statistical properties of maintaining low type I and type II error rates at relatively large proportions of missingness. The general applicability of the proposed method is demonstrated within a lead exposure study to assess the association between lead exposure and neurological motor function.

Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurements of individuals' latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychometric properties, where the number of measured constructs (i.e., factors) is assessed, and, afterward, rotational freedom is resolved to interpret these factors. This study assessed the effects of an acquiescence response style (ARS) on EFA for unidimensional and multidimensional (un)balanced scales. Specifically, we evaluated (a) whether ARS is captured as an additional factor, (b) the effect of different rotation approaches on the content and ARS factors recovery, and (c) the effect of extracting the additional ARS factor on the recovery of factor loadings. ARS was often captured as an additional factor in balanced scales when it was strong. For these scales, ignoring extracting this additional ARS factor, or rotating to simple structure when extracting it, harmed the recovery of the original MM by introducing bias in loadings and cross-loadings. These issues were avoided by using informed rotation approaches (i.e., target rotation), where (part of) the rotation target is specified according to a priori expectations on the MM. Not extracting the additional ARS factor did not affect the loading recovery in unbalanced scales. Researchers should consider the potential presence of ARS when assessing the psychometric properties of balanced scales and use informed rotation approaches when suspecting that an additional factor is an ARS factor. ispartof: EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT vol:83 issue:3 pages:433-472 ispartof: location:United States status: published

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase.

The ability to reduce current and future taxable income with prior years' taxable losses is highly relevant for explaining firms' effective tax rates. Compustat data on the tax loss carryforward (TLCF) are, however, often missing. We propose a method to estimate values for the missing TLCF data instead of the common practice in the literature of imputing zero values. In order to assess the accuracy of our method, we compare our estimated TLCFs with both a random selection of 10-K data and Compustat data for firm-years where Compustat data is available. The results show that our estimated values align very closely with the reported data. We re-analyze two existing studies using these estimated values. With the first, we show that imputing our estimated values instead of zeros leads to a large decrease in measurement error. This reduces the risk that firms with missing data and low effective tax rates are incorrectly classified as tax aggressive. The second re-analysis shows that using our estimated TLCFs leads to economically and statistically different conclusions compared to imputing zeros. Using our estimated values thus increases the probability of correct inferences in studies that use Compustat TLCF data. The estimated values are available from https://doi.org/10.34894/N9J1WE.

Tüketici davranışlarının değişmesiyle birlikte lüks ile ilgili yeni kavramların ortaya çıktığı görülmektedir. Herkese uygun bir lüks seçeneğinin olduğu düşünülecek olursa, tüketicilerin lüks markayı nasıl algıladığı üzerinde de durulması gerekliliği ortaya çıkmaktadır. Bu nedenle, bu çalışmada tüketicilerin lüks marka algısının lüks ürün satın alma niyetine etkileri üzerinde durulmaktadır. Çevrimiçi anket yöntemiyle 426 katılımcıya ulaşılmıştır. Toplanan veri SPSS Amos 24 ile incelenmiştir. Birinci el ve ikinci el lüks ürün satın alan tüketiciler kıyaslanmıştır. Araştırmanın sonucuna göre birinci el lüks ürün satın alan tüketicilerin genel lüks marka algısı, lüks ürün satın alma niyeti, finansal marka algısı ve sosyal marka algısı arasında anlamlı bir ilişki tespit edilmiştir. Fonksiyonel marka algısı ile benzer bir etki görülmemiştir. İkinci el lüks ürün satın alan tüketicilere bakıldığında ise genel lüks marka algısı ile sosyal marka algısı arasında anlamlı bir ilişki tespit edilmesine rağmen fonksiyonel, finansal ve lüks ürün satın alma niyeti arasında benzer etkiye rastlanılmamıştır., With consumer behaviours changing, new notions of luxury appear to be emerging. Given that there is a luxury option that suits everyone, the need to focus on how consumers perceive the luxury brand is emerging. Therefore, this study focuses on the effects of consumers' perception of luxury brands on their intention to buy luxury products. The online survey method reached 426 respondents. Data were examined with SPSS Amos 24. Consumers buying first-hand and second-hand luxury products have been benchmarked. Consumers buying first-hand luxury items, according to the study's conclusion; a meaningful relationship has been identified between general luxury brand perception, intention to buy luxury products, financial brand perception and social brand perception. No similar effect has been seen with functional brand perception. When looking at consumers buying second-hand luxury products, there has been no similar effect between functional, financial and luxury intention to purchase, although there has been a significant association between general luxury brand perception and social brand perception.

Se revisan los enterramientos femeninos con análisis antropológico de las necrópolis iberas de Alicante, Murcia y Albacete. El objetivo es identificar patrones de comportamiento en la composición de los ajuares a través de la individualización de los objetos, para lo que se ha recurrido a la aplicación de procedimientos cuantitativos sobre los datos extraídos. La reducida muestra de análisis antropológicos ha dificultado en gran medida la tarea. No obstante, los resultados estadísticos han destacado algunos objetos o aspectos de las tumbas femeninas, como elementos textiles, las armas, la asociación mujer-fusayola-textil, los individuos infantiles que acompañan a ciertas mujeres y la presencia de tabas, sobre los que exponemos algunas reflexiones. The female burials with anthropological analysis of the Iberian necropolis of Alicante, Murcia and Albacete are reviewed. The aim is to identify patterns of behaviour in the composition of the grave goods through the individualisation of the objects, for which we have resorted to the application of quantitative procedures on the data extracted. The small sample of anthropological analyses made this task very difficult. Nevertheless, the statistical results have highlighted some objects or aspects of the feminine tombs, such as textile elements, weapons, the association woman-spindle whorl-textile, children accompanying certain women and the presence of tabas, on which we present some reflections.

O estudo propôs um modelo experimental da corrupção, para investigar o efeito da magnitude e da probabilidade da punição, em situação experimental. O experimento simulou o ato corrupto típico, em um jogo com estudantes universitários e foi dividido em duas fases, com uma sessão cada uma, também para avaliar o efeito da exposição à contingência de punição. O participante deveria escolher entre duas alternativas: uma com reforçadores de menor magnitude sem punição associada, e outra com reforçadores de maior magnitude, com punição manipulada em sete condições experimentais. Os achados permitem concluir que houve inicialmente redução maior do comportamento indesejável com a magnitude da pena, porém, após contato prolongado com a contingência, e melhor discriminação das probabilidades em vigor, observou-se efeito crescente da probabilidade. Houve efeito de ordem das condições apresentadas com recuperação das respostas nos grupos em que a punição foi aplicada de forma decrescente.

Este estudo replicou sistematicamente o Experimento 1 de Carreiro (2017), testando os efeitos de valores extremos de magnitude e probabilidade da punição (perda de pontos); e do histórico de aprendizagem de respostas legais e ilegais (corruptas). Em um cenário virtual, 96 universitários escolheram entre: uma alternativa legal, que disponibilizou reforçadores (pontos) de baixa magnitude; e uma ilegal, que produziu reforçadores de alta magnitude, com possibilidade de punição. Foram cinco fases (com diferentes valores de punição e com linha de base) em dezesseis diferentes ordens, compondo dezesseis grupos. Os resultados sugerem eficácia da punição para redução do comportamento ilegal. A análise da lei da igualação demonstrou alta correlação entre a razão de respostas ilegais e razão de reforços, viés, e principalmente, igualação e sub igualação. Os valores e ordens que o estímulo punitivo foi apresentado produziram diferentes efeitos no comportamento ilegal, como menor proporção nas fases de alta magnitude e probabilidade

The modelling process is an essential part of any probabilistic approach. In this article, we propose three different categories of modelling approaches involving a probabilistic model that can be encountered in French secondary education: one based on the Laplacian approach to probability, another on the frequentist approach and the last one based on informal statistical inference. Based on the work of modelling of the German current, the objective of this article is to characterise the three categories of approaches and to identify what place and role statistics plays in these categories. The theoretical reflection is illustrated by an a priori analysis of examples of modelling problems. The characterisation is based on the different stages of the chosen modelling cycle and the associated working and model assumptions. La modélisation est un élément essentiel de toute démarche probabiliste. Dans cet article, nous proposons trois catégories différentes de démarches de modélisation faisant intervenir un modèle probabiliste pouvant être rencontrées dans l’enseignement secondaire français : l’une prenant appui sur l’approche laplacienne des probabilités, une autre sur l’approche fréquentiste et enfin la dernière relevant de l’inférence statistique informelle. S’appuyant sur les travaux autour de la modélisation du courant allemand, l’objectif est de caractériser les trois catégories de démarches et d’identifier quelle place et quel rôle joue la statistique dans ces catégories. La réflexion théorique est illustrée par une analyse a priori d’exemples de problèmes de modélisation. La caractérisation se fait en relation avec les différentes étapes du cycle de modélisation choisi et les hypothèses de travail et de modèle associées.

Travel time variability (TTV) plays a significant role in analysing the reliability of public transit system. The research works carried out on travel time variability under Indian traffic conditions are very few and these studies did not analyse the performance of travel time distribution in detail, considering different temporal and spatial aggregations. In this study, travel time variability is analysed using travel time distributions considering different temporal and spatial aggregations. The Automatic Vehicle Location (AVL) data of four transit routes of Mysore City, Karnataka, India are used to evaluate travel time distributions with respect to temporal aggregations (peak period, off-peak period, 60 minutes, 30 minutes and 15 minutes) and spatial aggregations (route level and segment level). The performance of travel time distributions is analysed using the Anderson-Darling (AD) test. The segments with signalised intersections and different land-use types are analysed to evaluate the distribution fit for various conditions. The results of both route and segment level analysis report highest accuracy and robustness values for Generalised Extreme Value (GEV) distribution. The distribution is proved to be superior in describing travel time variability of public transit.

This paper studies the optimality of matched-pair designs in randomized controlled trials (RCTs). Matched-pair designs are examples of stratified randomization, in which the researcher partitions a set of units into strata based on their observed covariates and assign a fraction of units in each stratum to treatment. A matched-pair design is such a procedure with two units per stratum. Despite the prevalence of stratified randomization in RCTs, implementations differ vastly. We provide an econometric framework in which, among all stratified randomization procedures, the optimal one in terms of the mean-squared error of the difference-in-means estimator is a matched-pair design that orders units according to a scalar function of their covariates and matches adjacent units. Our framework captures a leading motivation for stratifying in the sense that it shows that the proposed matched-pair design additionally minimizes the magnitude of the ex-post bias, i.e., the bias of the estimator conditional on realized treatment status. We then consider empirical counterparts to the optimal stratification using data from pilot experiments and provide two different procedures depending on whether the sample size of the pilot is large or small. For each procedure, we develop methods for testing the null hypothesis that the average treatment effect equals a prespecified value. Each test we provide is asymptotically exact in the sense that the limiting rejection probability under the null equals the nominal level. We run an experiment on the Amazon Mechanical Turk using one of the proposed procedures, replicating one of the treatment arms in Dellavigna and Pope (2018), and find the standard error decreases by 29%, so that only half of the sample size is required to attain the same standard error.

Statistical offices frequently use cutoff sampling to determine which businesses in a population should be surveyed. Examples include business surveys about international trade, production, innovation, ICT usage and so on. Cutoff thresholds are typically set in terms of key variables of interest and aim to satisfy a minimum coverage ratio–the share of aggregate values of reporting units. In this article we propose a simple cost-benefit approach to determination of the sampling cutoff by taking into account the response burden. In line with existing practice, we use the coverage ratio as our measure of accuracy and provide either analytical or numerical solutions to cutoff determination. Using a business survey on response burden of reporting trade flows within the EU (Intrastat), we present an application that illustrates our approach to cutoff determination. An important practical implication is the possibility to set industry-contingent cutoffs.

This article used nine patent value indicators to compare the differences in the characteristics of U.S. patents for which provisional applications (PAs) were submitted and other patents without PAs (NPAs). The findings revealed significant differences in the average numbers of backward and forward citations, patent and nonpatent references, and patent claims, patent family size (number of patents and number of countries), and duration of examination between the two groups of patents granted between 2005 and 2017 by United States Patent and Trademark Office. Increasing trends were observed in the average number of backward citations and average percentage of patents renewed per year in both PA and NPA groups, whereas decreasing trends were observed in the average numbers of forward citations, countries, claims, and average duration of examination per patent and per year in PA and NPA groups. Differences in patent characteristics based on type and field were observed. This article also revealed more significant differences in PAs than in NPAs because of a larger number of pairs of fields. The results of this study confirmed that PAs have a stronger association with patent value and provided further justification for filing PAs, in addition to obtaining an early effective date for new inventions.

Workplaces can be associated with occupational stress, detrimental consequences in terms of loss of health and reduced psychosocial well-being. Importantly, employees may be particularly at risk of poorer well-being during times of adversity at work, when not able to apply adaptive coping strategies and adopt a more empathetic approach. This study aimed to develop a scale to estimate occupational stress both in terms of situational and individual components, by performing item selection, internal reliability assessment, and investigation of the ceiling/floor effect. The target population consisted of veterinarians (n = 116), based on evidence of high risk of occupational stress and related mental distress. Out of twenty initial candidate entries, exploratory factor analysis retained fifteen items consisting of three domains related to occupational stress, copying strategies, and empathy (SCOPE). The SCOPE scale demonstrated good internal consistency as a whole (Cronbach’s alpha = 0.79) and when considering the three subscales (stress, 0.85; coping, 0.77; and empathy, 0.71). On a possible range from 15 (worst adjustment) to 75 (best adjustment), the sample mean performance was 51.68 (SD, 8.50). Preliminary evidence indicated that the SCOPE questionnaire may reveal differential effects of type of work on levels of occupational stress and related coping and empathy skills.

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstrap and simulation techniques from LMM paradigm can be used to construct the confidence intervals. The purpose of this research is to examine four different LMM-based methods for computing the confidence intervals that have been proposed and to determine their accuracy under six simulated conditions based on the type of test scores (normal, dichotomous, and polytomous data) and data measurement design ( p× i× r and p× [ i:r]). A bootstrap technique called “parametric methods with spherical random effects” consistently produced more accurate confidence intervals than the three other LMM-based methods. Furthermore, the selected technique was compared with model-based approach to investigate the performance at the levels of variance components via the second simulation study, where the numbers of examines, raters, and items were varied. We conclude with the recommendation generalizability coefficients, the confidence interval should accompany the point estimate.

La bibliothèque de l’Insee, héritière des fonds des bibliothèques des anciens services statistiques publics français, est aujourd’hui un gisement documentaire peu exploité par les chercheurs. Elle est cependant incontournable pour la recherche statistique, mais aussi historique et sociale en développant toutes les thématiques visées par des études statistiques. Il est ainsi possible de retracer près de deux siècles d’observation des sociétés, à la fois françaises et étrangères. La diversité des collections de la bibliothèque et sa couverture internationale en font une bibliothèque hors normes, à la mission de conservation croissante. Le défi pour les années à venir est de promouvoir et faire redécouvrir ce fonds documentaire qui peut être une ressource d’un intérêt considérable pour les chercheurs. Having inherited much of its holdings from the collections of former French public statistical services, the library of Insee is today a documentary deposit rarely used by researchers. It is nevertheless key to statistical research. To the degree they interest themselves in the subjects of statistical study, this also holds true for historical and social research. It is thus possible to retrace nearly two centuries of social observation, French and foreign alike. The diversity of the library’s collections and its international coverage make for an unconventional collection with an expanding mission of preservation. In the years to come, the challenge will be to promote and raise awareness about it among researchers for whom it may represent a resource of significant interest.

Publicación diaria acerca del movimiento bursátil del Mercado Integrado Latinoamericano (Perú, Chile, Colombia y México), así como de los principales índices del mundo.