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To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.

Objectives: Consumer data privacy on e-commerce platforms is increasingly crucial. This study aims to investigate privacy protection mechanisms, particularly focusing on personal and corporate secrets. It seeks to understand individual perspectives on privacy and preferences for data disclosure. The primary objective is to explore methods for safeguarding personal information while maintaining data integrity. Methods/Analysis: We employ non-parametric statistical techniques to analyze consumer behavior and preferences on e-commerce platforms. This involves examining patterns of data disclosure and identifying sensitive information shared by users. By studying communication dynamics and recording practices, we assess the efficacy of current privacy protection measures. Novelty/Improvement: This study contributes to the understanding of consumer privacy protection by emphasizing the importance of non-parametric statistical methods in e-commerce research. Our findings underscore the need for enhanced privacy measures. We advocate for further research and development of innovative privacy-enhancing technologies to address evolving privacy challenges in online commerce. Findings:Our research highlights the significance of personal privacy concerns in e-commerce settings. We identify a spectrum of privacy attitudes among users, ranging from strict confidentiality to selective disclosure. Furthermore, our analysis reveals potential vulnerabilities in current privacy safeguards, particularly regarding the collection and storage of sensitive data on e-commerce platforms. Doi: 10.28991/HIJ-2024-05-02-013 Full Text: PDF

Summary: Marc Hallin was born in Ghent, Belgium, on 23 April 1949. He holds a Licence en Sciences mathématiques (1971), a Licence en Sciences actuarielles (1972), and a Doctorat en Sciences (1976) from the Université libre de Bruxelles. He then rose through the professorial ranks at the same institution, being successively Premier Assistant (1977–1978), Chargé de Cours associé (1978–1984), Chargé de Cours (1984–1988), Professeur ordinaire (1988–2009), and Professeur ordinaire émérite upon retirement in 2009. Throughout his career, he supervised 25 PhD students and held invited positions at many institutions of high standing in Austria, Belgium, England, France, Hong Kong, Italy, Portugal, Spain, Switzerland, and the USA (most notably Princeton). A renown expert in time series analysis, econometrics, and non‐parametric inference, Marc is the author or coauthor of over 250 research papers, for which he received numerous awards, including the Medal of the Faculty of Mathematics and Physics of Charles University in Prague (2006), a Humboldt Forschungspreis from the Alexander von Humboldt Foundation (2012), the Pierre‐Simon de Laplace Award of the Société française de Statistique (2022), and the Gottfried E. Noether Distinguished Scholar Award of the American Statistical Association (2022). He gave several distinguished lecture series, including the 2017 Hermann Otto Hirschfeld Lecture Series at the Humboldt Universität zu Berlin, and the 2018 Mahalanobis Memorial Lecture at the Indian Statistical Institute. Over the years, he co‐edited a dozen books and proceedings, and served on the editorial boards of several journals, including the Journal of Time Series Analysis (1994–2009), the Journal of Econometrics (2013–2019), the Journal of Business and Economic Statistics (2018–), and the Theory and Methods Section of the Journal of the American Statistical Association (2005–). He is a Fellow of the Institute of Mathematical Statistics (1990) and the American Statistical Association (1997), as well as a member of the Classe des Sciences of the Royal Academy of Belgium (1999). Marc has been a member of the International Statistical Institute since 1985 and was (co‐) Editor‐in‐Chief of the International Statistical Review from 2010 to 2015. [ABSTRACT FROM AUTHOR]

Self-proficiency, distinct from self-efficacy, is a more comprehensive dimension in assessing students’ abilities. Moreover, the TAI cooperative learning model is believed to be relevant in enhancing self-proficiency, as well as other mathematical ability dimensions, particularly in Statistics course. Therefore, this study aimed to assess the enhancement of self-proficiency, mathematical problem-solving and mathematical communication by implementing the TAI (Team-Assisted Individualization) Cooperative Learning Model in the context of Non-Parametric Statistics, which is a core undergraduate statistics course. A quasi-experimental approach with purposive sampling, which comprised 72 observation samples divided into control and experimental groups, was used to conduct this study. Evaluation instruments consisted of mathematical problem-solving test, mathematical communication test, and self-proficiency test, along with observation sheets. Furthermore, these statistical analysis tools were used included the Paired Sample t-test, the Independent Sample t-test, as well as the ANCOVA (Analysis of Covariance). The results, obtained through various assessments that examined mean differences and comprehensive statistical analyses, comprised both independent and dependent sample evaluations. The evaluations consistently showed substantial increase in problem-solving ability, communication skills, and self-proficiency in the cohort subjected to the experimental conditions, surpassing those observed in the control group. The ANCOVA estimations further validated the meaningful effect of the teaching strategy on the enhancement of the tested aspects. In conclusion, this study implied that the implementation of TAI learning model effectively enhanced all three objectively and measurably assessed aspects, particularly in the context of non-parametric statistics course.

One approach to the design, experimentation, parameter setting, and assembly of an X-ray generator get performed to reduce radiation emission (microgray μ G y ) without neglecting the power of the beam emission (KVp). A combination gets made in the manufacture of the transformer coil between turns of the secondary winding S k , with the number of turns of the filament F i . As a result, we obtained a transformer with an emissivity between the desired parameters and the optimum beam power. We classify feasible and infeasible cases in the assembly of X-ray devices using support vector machines. In addition, the tools used for statistical inference were non-parametric tests, such as the modified Friedman test and post-hoc Quade and Conover tests. Finally, we obtained a good image with an excellent resolution without exposing people to high radiation emissions. [ABSTRACT FROM AUTHOR]

Introduction: The interaction of genotype with the environment provides the possibility of selecting stable genotypes for a wide range of environments. Evaluation of the interaction of genotype with the environment is necessary to increase the efficiency of selecting varieties with high and stable performance in a wide range of different environments. The objectives of this paper are: (1) evaluating the stability of 60 potato genotypes for tuber yield in two years using parametric and non-parametric stability methods, (2) identifying genotypes with good and stable performance when evaluated in variable environments and (3) investigating the relationship and correlation between stability statistics of tuber performance under water deficit conditions in Iran. Materials and methods: To evaluate the performance stability and adaptability of the 60 potato genotypes, two cultivars and 58 advanced clones, 17 parametric and non-parametric statistics were evaluated for tuber yield across eight environments during the 2018-2019 growing seasons. The genotypes were evaluated under normal and water deficit conditions in Karaj and Ardabil. In this study, the parametric analysis for yield was determined by such parameters as regression coefficient (bi), environmental variance (Si2), coefficient of variation (CVi), deviation from regression (sdi2), and Wricke's ecovalence (Wi2), The non-parametric analysis included Nassar and Huhn's statistics (S(1) and S(2)), Huhn's equation (S(3) and S(6)), Shukla's stability variance (σ2i), Plaisted and Peterson's (θi), Thennarasu's non-parametric (NP(1), NP(2), NP(3), and NP(4)), and Kang's rank-sum (KR) parameter. Parametric and non-parametric statistics are used by agronomists and plant breeders. Currently, researchers are interested in applying several stability statistics to obtain the desired results, crucial for the selection of stable varieties Results and discussion: Composite variance analysis showed that the effect of place and year as well as the effect of genotype are significant. The interaction effect of year × location × genotype was significant at the probability level of 1%. The genotype effect was also significant at the 1% probability level. The interaction effect of genotype x location and genotype x year was not significant, which indicates that the average performance of genotypes is not different in different locations and years. Grouping of genotypes based on average performance and parametric and non-parametric stability statistics showed that genotypes are divided into four main groups. In general, based on the average rank of parametric and non-parametric stability parameters, genotypes G31, G21 and G36 had the least changes and were recognized as the most stable genotypes, and therefore they can be introduced as stable genotypes. The results of stability statistics and cluster analysis showed that G31, G21 and G36 genotypes can be introduced as stable and compatible genotypes. Conclusion: Our results showed that G21, G31 and G36 genotypes contributed the least to the genetic × environment interaction (G*E) and were considered as stable genotypes under water deficit conditions. The different parametric and non-parametric stability procedures can be proposed to select drought tolerant genotypes under different environments conditions; these procedures could be useable for recognition of the best genotypes under drought stress conditions. Therefore, the analysis of yield stability could be utilized in combination with parametric and non-parametric methods to evaluate and identify drought tolerance genotypes. Dendrogram results confirmed each other with the results of parametric and nonparametric statistics. While G49, G51, G53 and G56 genotypes with the highest values were the most unstable genotypes. [ABSTRACT FROM AUTHOR]

Ichimoku Kinkohyo or Ichimoku Cloud Chart is one of the most popular technical indicators used by traders all over the world. However, its profitability is heavily influenced by the market environment, to which it is applied. Furthermore, the COVID-19 outbreak may have an impact on the market environment as well as the performance of all technical indicators. This study is the first to look into the profitability of Ichimoku-based trading rules in the Vietnamese stock market in the context of the COVID-19 outbreak. More particularly, the COVID-19 outbreak has a positive influence on the performance of this strategy when considering the entire market as well as a variety of industries including real estate industry, food and beverage industry, resource industry, and automotive and electronic components industry. Compared to the pre-pandemic period, the return on investment obtained per each transaction using the Ichimoku-based strategy increased by roughly 8 - 9 % in the pandemic period. Compared to the Buy-and-hold method, the Ichimoku-based strategy could slightly increase Accumulated return while posing a lower risk. The findings indicate that the Ichimoku-based strategy is applicable to the Vietnam stock market, regardless of the adverse effects of the pandemic on the industries. [ABSTRACT FROM AUTHOR]

The k-sample problem for functional data has been widely studied from theoretical and applied perspectives. In literature, Gaussianity of the generating process is generally assumed, which may be impractical in some situations. This work proposes an extension of the Kruskal-Wallis test to the case of functional data as an alternative to the problem of non- Gaussianity. The methodology used consisted of transforming each group's functional data into scalars using random projections and subsequently performing classical Kruskal-Wallis tests. The main results were the extension of the Kruskal-Wallis test to the case of functional data and the verification of its unbiased and consistency properties. Reducing dimensionality from random projections allows us to extend the classical Kruskal-Wallis test to the functional context and solve problems of non-Gaussianity and atypical observations.

This article is devoted to methods of processing random processes. This task becomes particularly relevant in cases where the random process is broadband and non-stationary; then, the measurement of a random process can be associated with an assessment of its probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation with a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real-time pace (for example, radio telemetry systems in spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods, to determine probability characteristics in this case is not possible. In addition, the on-board computing systems of spacecraft operate under conditions of restrictions on mass-dimensional characteristics and energy consumption. Therefore, there is a need to apply accelerated methods of processing measured random processes. This article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall's statistics is considered, as are methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. This article presents the results of statistical modeling using the Mathcad program. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate whether the rally length in high-level Spanish volleyball was longer in women than in men. A total of 1,786 rallies were observed: 792 for women and 994 for men. The recorded variables were match (quarter-final 1, quarter-final 2, semi-final 1, semi-final 2, final), gender (men, women), rally length (seconds), pseudo-rally (no, yes), and terminal event (ball out of sight, ball in/out, fault). Different non-parametric statistical techniques were used to compare the rally length between groups or subsets of data, i.e., the Kruskal-Wallis H test, the Mann-Whitney U test, quantile regression, and survival analysis. The mean and median rally length was significantly and slightly longer in women than in men. The rally length difference between genders was barely 1 s in quantile 0.5 or median, while in quantile 0.95, it was just over 4 s. In women, the probability of ending the rally at 3.9, 5.1, 10.2, and 43.9 s (at 4.4, 6.3, 11.6, and 43.9 s without pseudo-rallies) was 25%, 50%, 75%, and 100%, respectively. In men, the probability of ending the rally at 3.2, 4.3, 7.9, and 29.1 s (at 3.9, 4.8, 8.8, and 29.1 s without pseudo-rallies) was 25%, 50%, 75%, and 100%, respectively. These temporal thresholds can help volleyball coaches to train their players in a coherent manner. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised Bsplines. We show that this projection defines a GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at considerably lower runtimes. We apply the model to weekly, age-stratified COVID-19 attributable deaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The Bsplines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors. [ABSTRACT FROM AUTHOR]

We introduce a new methodology to investigate the degree of persistence in firm growth dynamics, based on Conditional Quantile Transition Probability Matrices (CQTPMs) and exact inferential tests derived from two well-known mobility indexes. We apply the methodology to study manufacturing firms in the UK and four major European economies over the period 2010–2017. We find that CQTPMs display more persistence than under a fully independent firm growth process, albeit considerable turbulence and significant bouncing effects are detected. Exploiting the inferential statistics within a regression framework, we show that productivity, openness to trade, and business dynamism are the primary sources of firm growth persistence across sectors, while country-specific and time-specific factors play a second-order role. Plain English Summary: Use mobility indexes from CQTPMs to make precise inference about persistence in firm growth performance! This paper proposes a new method to measure the degree of persistence in the growth process of firms. Improving upon previous studies, we provide exact statistical inference and control for possible confounding factors. Application of our method to firm-level data on the UK and four major European economies, reveals a statistically significant, albeit small tendency of firms to repeat their past growth. However, we also observe considerable turbulence in the growth rates distribution over time. Persistence in growth is more strongly associated with sectoral characteristics such as productivity, business dynamism and trade openness, than with country-specific or time-specific factors. These findings pose interesting challenges to the theory. They also imply that policies addressed to foster growth in a specific group of firms are likely to have a volatile and transient effect. Our approach is flexible and suited for wide applicability in other domains of firm-level analysis. [ABSTRACT FROM AUTHOR]

Constitutive equations in a nuclear reactor safety analysis code are mostly empirical correlations developed from experiments, which always accompany uncertainties. The accuracy of the code can be improved by modifying the constitutive equations fitting wider range of data with less uncertainty. Thus, the sensitivity of the code with respect to the constitutive equations is evaluated quantitatively in the paper to understand the room for improvement of the code. A new methodology is proposed which first starts by dividing the thermal hydraulic conditions into multiple sub-regimes using self-organizing map (SOM) clustering method. The sensitivity analysis is then conducted by multiplying an arbitrary set of coefficients to the constitutive equations for each sub-divided thermal-hydraulic regime with SOM to observe how the code accuracy varies. The randomly chosen multiplier coefficient represents the uncertainty of the constitutive equations. Furthermore, the set with the smallest error with the selected experimental data can be obtained and can provide insight which direction should the constitutive equations be modified to improve the code accuracy. The newly proposed method is applied to a steady-state experiment and a transient experiment to illustrate how the method can provide insight to the code developer.

Extragalactic water maser emissions at 22 GHz have been playing vital roles in astrophysics. The limited detection rate of these masers has been motivating researchers to find clues that can help characterise them. The physical environments 22 GHz masers formed in are still ambiguous. Accordingly, statistical studies have been thoroughly used to resolve these favourable environments. This work goes through the essential parameter of Active Galactic Nuclei (AGN), namely, the mass of the central supermassive black hole (M BH) of the maser host galaxy. We study the correlation between maser luminosity (L H 2 O) and M BH in sub-samples of megamasers (MMs), kilomasers (KMs), and disc masers. The regression line of the relation is also calculated for these sub-samples. Unlike the results of previous works, dividing the maser sample into MMs and KMs gives no privilege to MM galaxies. Contrary to expectation, KMs have weak and low significant L H 2 O - M BH correlation, while MMs show no correlation. The positive correlation in KMs can be explained by the role of AGN therein, while the diversity of MMs types, with some of which are not strongly related to AGN, may explain the correlation missing. Surprisingly, the 28 disc maser sample, where tight correlation is expected, shows a very weak and low significant L H 2 O -M BH correlation. Future VLBI studies will eventually lead to a specific classification of a good number of maser galaxies, which is essential to establishing the L H 2 O -M BH relation. [ABSTRACT FROM AUTHOR]

This paper provides new evidence on the quantification of economic losses and/or gains from skilled human capital mobility in terms of GDP and productivity in several EU countries during and after the Great Recession. We construct two novel indicators to quantify and compare the economic effects of human capital mobility across EU countries in 2008, 2012, and 2016. Through hierarchical clustering, we created groups of countries to perform a non-parametric MANOVA. The results suggest the existence of three groups of countries aggregated by similar economic and mobility patterns. The differences between these groups are significant and allow us to delineate the countries' gains and losses. [ABSTRACT FROM AUTHOR]

Performance indicators that contributed to success at the group and play-off stages of the 2019 Rugby World Cup were analysed using publicly available data obtained from the official tournament website using both a non-parametric statistical technique, Wilcoxon’s signed rank test, and a decision rules technique from machine learning called RIPPER. Our statistical results found that ball carry effectiveness (percentage of ball carries that penetrated the opposition gain-line) and total metres gained (kick metres plus carry metres) were found to contribute to success at both stages of the tournament and that indicators that contributed to success during group stages (dominating possession, making more ball carries, making more passes, winning more rucks, and making less tackles) did not contribute to success at the play-off stage. Our results using RIPPER found that low ball carries and a low lineout success percentage jointly contributed to losing at the group stage, while winning a low number of rucks and carrying over the gain-line a sufficient number of times contributed to winning at the play-off stage of the tournament. The results emphasise the need for teams to adapt their playing strategies from group stage to the play-off stage at the tournament in order to be successful.

Abstract Geoprocessing techniques are generally applied in natural disaster risk management due to their ability to integrate and visualize different sets of geographic data. The objective of this study was to evaluate the capacity of classification and regression tree (CART) to assess fire risk. MCD45A1 product of the burnt area, relative to a 16-year period (2000-2015) was used to obtain a fire occurrence map, from center points of the raster, using a kernel density approach. The resulting map was then used as a response variable for CART analysis with fire influence variables used as predictors. A total of 12 predictors were determined from several databases, including environmental, physical, and socioeconomic aspects. Rules generated by the regression process allowed to of define different risk levels, expressed in 35 management units, and used to produce a fire prediction map. Results of the regression process (r = 0.94 and r² = 0.88) demonstrate the capability of the CART algorithm in highlighting hierarchical relationships among predictors, while the model’s easy interpretability provides a solid basis for decision making. This methodology can be expanded in other environmental risk analysis studies and applied to any area of the globe on a regional scale.

The Valley of Pichis is an area of Peru with a mostly rural population and a great variety of flora and fauna. However, for many years there has been a trend towards deforestation that is increasingly worrying. Due to the overexploitation of pastures due to the intensive grazing that is carried out by the inhabitants of the area, it has been perceived that there are areas where the pastures are dry and in others there is a lack of pastures to continue feeding the local cattle. The lack of scientific culture on the part of the inhabitants of the area who are not capable of carrying out a more rational grazing is also appreciated. That is why it was decided to study the introduction of three plant species that can serve as substitutes for native species in order to provide a greater opportunity for regeneration of these species and thus avoid desertification of the area. The data obtained from the results of the experiment were processed with the help of the Kruskal-Wallis method, which consists of comparing the results of more than two independent populations, to determine if there is a significant difference between them, or if instead they respond to the same distribution. This is a non-parametric test and therefore has the advantage that it does not have the normality of the populations as a hypothesis. [ABSTRACT FROM AUTHOR]

One of the main features of interest in analysing the light curves of stars is the underlying periodic behaviour. The corresponding observations are a complex type of time series with unequally spaced time points. The main tools for analysing these type of data rely on the periodogram-like functions constructed with a desired feature so that the peaks indicate the presence of a potential period. We explore a particular periodogram for the irregularly observed time series. We identify the potential periods by implementing the saddlepoint approximation, as a faster and more accurate alternative to the simulation based methods that are currently used. The power analysis of the testing methodology is reported together with applications using light curves from the Hunting Outbursting Young Stars citizen science project. [ABSTRACT FROM AUTHOR]

Some scholars build models to classify documents into chosen categories. Others, especially social scientists who tend to focus on population characteristics, instead usually estimate the proportion of documents in each category—using either parametric "classify-and-count" methods or "direct" nonparametric estimation of proportions without individual classification. Unfortunately, classify-and-count methods can be highly model-dependent or generate more bias in the proportions even as the percent of documents correctly classified increases. Direct estimation avoids these problems, but can suffer when the meaning of language changes between training and test sets or is too similar across categories. We develop an improved direct estimation approach without these issues by including and optimizing continuous text features, along with a form of matching adapted from the causal inference literature. Our approach substantially improves performance in a diverse collection of 73 datasets. We also offer easy-to-use software that implements all ideas discussed herein. [ABSTRACT FROM AUTHOR]

Abstract Background Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. Results To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. Conclusions We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

This article is devoted to methods of processing random processes. This task becomes particularly relevant in cases where the random process is broadband and non-stationary; then, the measurement of a random process can be associated with an assessment of its probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation with a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real-time pace (for example, radio telemetry systems in spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods, to determine probability characteristics in this case is not possible. In addition, the on-board computing systems of spacecraft operate under conditions of restrictions on mass-dimensional characteristics and energy consumption. Therefore, there is a need to apply accelerated methods of processing measured random processes. This article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall’s statistics is considered, as are methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. This article presents the results of statistical modeling using the Mathcad program.

There is an increasing interest in the use of win ratio with composite time‐to‐event due to its flexibility in combining component endpoints. Exploring this flexibility further, one interesting question is in assessing the impact when there is a difference in treatment effect in the component endpoints. For example, the active treatment may prolong the time to occurrence of the negative event such as death or ventilation; meanwhile, the treatment effect may also shorten the time to achieving positive events, such as recovery or improvement. Notably, this portrays a situation where the treatment effect on time to recovery is in a different direction of benefit compared to the time to ventilation or death. Under such circumstances, if a single endpoint is used, the benefit gained for other individual outcomes is not counted and is diminished. As consequence, the study may need a larger sample size to detect a significant effect of treatment. Such a scenario can be handled by win ratio in a novel way by ranking component events, which is different from the usual composite endpoint approach such as time‐to‐first event. To evaluate how the different directions of treatment effect on component endpoints will impact the win ratio analysis, we use a Clayton copula‐based bivariate survival simulation to investigate the correlation of component time‐to‐event. Through simulation, we found that compared to the marginal model using single endpoints, the win ratio analysis on composite endpoint performs better, especially when the correlation between two events is weak. Then, we applied the methodology to an infectious disease progression simulated study motivated by COVID‐19. The application demonstrates that the win ratio approach offers advantages in empirical power compared to the traditional Cox proportional hazard approach when there is a difference in treatment effect in the marginal events. [ABSTRACT FROM AUTHOR]

Performance indicators that contributed to success at the group and play-off stages of the 2019 Rugby World Cup were analysed using publicly available data obtained from the official tournament website using both a non-parametric statistical technique, Wilcoxon's signed rank test, and a decision rules technique from machine learning called RIPPER. Our statistical results found that ball carry effectiveness (percentage of ball carries that penetrated the opposition gain-line) and total metres gained (kick metres plus carry metres) were found to contribute to success at both stages of the tournament and that indicators that contributed to success during group stages (dominating possession, making more ball carries, making more passes, winning more rucks, and making less tackles) did not contribute to success at the play-off stage. Our results using RIPPER found that low ball carries and a low lineout success percentage jointly contributed to losing at the group stage, while winning a low number of rucks and carrying over the gain-line a sufficient number of times contributed to winning at the play-off stage of the tournament. The results emphasise the need for teams to adapt their playing strategies from group stage to the play-off stage at the tournament in order to be successful. [ABSTRACT FROM AUTHOR]

Background: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. Results: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. Conclusions: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics. [ABSTRACT FROM AUTHOR]

We study the non-parametric estimation of an unknown survival function S with support on R + based on a sample with multiplicative measurement errors. The proposed fully data-driven procedure is based on the estimation of the Mellin transform of the survival function and a regularization of the inverse of the Mellin transform by a spectral cut-off. The upcoming bias-variance trade-off is handled by a data-driven choice of the cut-off parameter. In order to discuss the bias term, we consider the Mellin–Sobolev spaces which characterize the regularity of the unknown survival function S through the decay of its Mellin transform. For the analysis of the variance term, we consider the independent and identically distributed case and incorporate dependent observations in form of Bernoulli shift processes and β-mixing sequences. Additionally, we show minimax optimality over Mellin–Sobolev spaces of the spectral cut-off estimator. [ABSTRACT FROM AUTHOR]

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method. [ABSTRACT FROM AUTHOR]

We aim at estimating the invariant density associated to a stochastic differential equation with jumps in low dimension, which is for d = 1 and d = 2. We consider a class of fully non-linear jump diffusion processes whose invariant density belongs to some Hölder space. Firstly, in dimension one, we show that the kernel density estimator achieves the convergence rate 1/T, which is the optimal rate in the absence of jumps. This improves the convergence rate obtained in Amorino and Gloter [J. Stat. Plann. Inference213 (2021) 106–129], which depends on the Blumenthal-Getoor index for d = 1 and is equal to (logT)/T for d = 2. Secondly, when the jump and diffusion coefficients are constant and the jumps are finite, we show that is not possible to find an estimator with faster rates of estimation. Indeed, we get some lower bounds with the same rates {1/T, (logT)/T} in the mono and bi-dimensional cases, respectively. Finally, we obtain the asymptotic normality of the estimator in the one-dimensional case for the fully non-linear process. [ABSTRACT FROM AUTHOR]

Objectives: Stimuli that are separated by a short window of space or time, known as spatial and temporal binding windows (SBW/TBWs), may be perceived as separate. Widened TBWs are evidenced in schizophrenia, although it is unclear if the SBW is similarly affected. The current study aimed to assess if dexamphetamine (DEX) may increase SBWs in a multimodal visuo-tactile illusion, potentially validating usefulness as an experimental model for multimodal visuo-tactile hallucinations in schizophrenia, and to examine a possible association between altered binding windows (BWs) and working memory (WM) suggested by previous research., Methods: A placebo-controlled, double-blinded, and counter-balanced crossover design was employed. Permuted block randomisation was used for drug order. Healthy participants received DEX (0.45 mg/kg, PO, b.i.d.) or placebo (glucose powder) in capsules. The Rubber Hand Illusion (RHI) and Wechsler Adult Intelligence Scale Spatial Span was employed to determine whether DEX would alter SBWs and WM, respectively. Schizotypy was assessed with a variety of psychological scales., Results: Most participants did not experience the RHI even under normal circumstances. Bi-directional and multimodal effects of DEX on individual SBWs and schizotypy were observed, but not on WM., Conclusions: Bidirectional multimodal effects of DEX on the RHI and SBWs were observed in individuals, although not associated with alterations in WM., (© 2024 The Author(s). Human Psychopharmacology: Clinical and Experimental published by John Wiley & Sons Ltd.)

The benefit–risk balance is a critical information when evaluating a new treatment. The Net Benefit has been proposed as a metric for the benefit–risk assessment, and applied in oncology to simultaneously consider gains in survival and possible side effects of chemotherapies. With complete data, one can construct a U-statistic estimator for the Net Benefit and obtain its asymptotic distribution using standard results of the U-statistic theory. However, real data is often subject to right-censoring, e.g. patient drop-out in clinical trials. It is then possible to estimate the Net Benefit using a modified U-statistic, which involves the survival time. The latter can be seen as a nuisance parameter affecting the asymptotic distribution of the Net Benefit estimator. We present here how existing asymptotic results on U-statistics can be applied to estimate the distribution of the net benefit estimator, and assess their validity in finite samples. The methodology generalizes to other statistics obtained using generalized pairwise comparisons, such as the win ratio. It is implemented in the R package BuyseTest (version 2.3.0 and later) available on Comprehensive R Archive Network. [ABSTRACT FROM AUTHOR]

Analysis of similarities (ANOSIM) is a robust non‐parametric hypothesis‐testing framework for differences in resemblances among groups of samples. To date, the generalisation and use of ANOSIM to analyse various 2‐way nested and crossed designs with unordered or ordered factors has been described. This paper describes how the 2‐way tests may be extended and modified for the analysis of 3‐way designs, including the introduction of a different type of constrained permutation procedure for a design in which one factor is nested in another and crossed with a third. The construction of 3‐way tests using the generalised statistic in various nested and crossed designs, with or without ordered factors, and with or without replication, is described. Applications of the new tests to ecological data are demonstrated using three marine examples. They are as follows: a study of changes in fish diet for fish of increasing size sampled in different locations at different times (a 3‐way fully crossed design with ordered factors); a hierarchical spatial study of the fauna inhabiting kelp holdfasts (a 3‐way fully nested design with unordered factors); and a study of infaunal macrobenthos in which sites within areas were resampled over a long time series (a design in which sites are nested in areas but crossed with years, both latter factors potentially being ordered). The magnitudes of the ANOSIM statistics provide information about relative effect sizes (accounting for other factors), which is often a focus for multifactorial designs. Though the described ANOSIM tests do not provide parallels for all the range of 3‐way mixed‐factor designs possible in ANOVA (and its multivariate semi‐parametric counterpart PERMANOVA), it is seen that for nested factors these ANOSIM tests parallel the matching PERMANOVA random‐effects models, and not their fixed‐effects counterparts, thus allowing the same broader inference about the space from which these random factor levels are drawn. [ABSTRACT FROM AUTHOR]

Ecological (isotopic) niche refers to a surface in a two-dimensional space, where the axes correspond to environmental variables that reflect values of stable isotopes incorporated in an animal's tissues. Carbon and nitrogen stable isotope ratios (δ13C-δ15N) notably provide precious information about trophic ecology, resource and habitat use, and population dynamics. Various metrics allow for isotopic niche size and overlap assessment. In this paper, we advocate α-minimum convex polygons (MCP) - that have long been used for home range estimation – as a relevant tool for isotopic niche size, overlap, and characteristics. The method allows for outlier rejection while being suited to data that are not Gaussian in the bivariate isotopic (δ13C-δ15N) space. The proposed indicators are compared to other existing approaches and are shown to be complementary. Notably an indicator of divergence within the niche is introduced, and allows for comparisons at low (n > 6) and different sample sizes. The R code is made publicly available and will enable ecologists to perform isotopic niche comparison, contraction and expansion assessment, and overlap, based on various methods.

The extropy measure is a complementary dual function of Shannon entropy which was proposed by Lad et al. Extropy: complementary dual of entropy. Stat Sci. 2015;30:40–58. This measure of uncertainty have received a considerable attention in the last five years. In this paper, several methods of estimation for the extropy and entropy measures based a progressively Type-II censored data are derived. Simulation studies have been performed to see the effectiveness of the proposed methods, and two real data sets have been analysed for illustrative purposes. Extropy and entropy-based tests of uniformity are also proposed along with their simulated critical values. The power values of the proposed tests are simulated and studied under various alternatives. [ABSTRACT FROM AUTHOR]

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (−0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change. • Climate variation is strongly impacted regional flood probability. • LST increased in the central Mumbai but declined in coastal region. • Geophysical attributes are major driving forces reflected in flood susceptibility. • Increased trend of urbanization was noticed in the last two decades. [ABSTRACT FROM AUTHOR]

Objective: The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) is recommended for evaluation of sensorimotor impairment post stroke, but the item-level reliability of the scale is unknown. The study aims to determine intra- and inter-rater reliability of the FMA-UE at item-, subscale- and total score level in patients with early subacute stroke. Design: Intra/inter-rater reliability. Subjects: Sixty consecutively included patients with stroke (mean age 65.9 years) admitted to Central Military Hospital of Colombia, Bogota. Methods: Two physiotherapists scored FMA-UE independently on 2 consecutive days within 10 days post stroke. A rank-based statistical method for paired ordinal data was used to assess the level of agreement, systematic and random disagreements. Results: Systematic disagreements either in position or concentration were detected in 4 items of the shoulder section. The item level intra- and inter-rater agreement was high (79100%). The 70% agreement was also reached for the subscales and the total score when 13-point difference was accepted. Conclusion: The FMA-UE is reliable both within and between raters in patients with stroke in the early subacute phase. A wider international use of FMA-UE will allow comparison of stroke recovery between regions and countries and thereby potentially improve the quality of care and rehabilitation in persons with stroke worldwide.

Random sets play an essential role in modelling several phenomena in biology, medicine and material science. However, sometimes it is hard to describe them using a specific model. Therefore it can also be difficult to classify them or to compare their realisations. This contribution proposes a similarity measure between two random sets whose realisations consist of many components based on just one realisation of each of them. The similarity measure is obtained in a non-parametric way taking into account the shapes and the positions of the components. The procedure is justified by a simulation study and consequently applied to real biomedical data of histological images of mammary tissue.

Even if large historical dataset could be available for monitoring key quality features of a process via multivariate control charts, previous knowledge may not be enough to reliably identify or adopt a unique model for all the variables. When no specific parametric model turns out to be appropriate, some alternative solutions should be adopted and exploiting non‐parametric methods to build a control chart appears a reasonable choice. Among the possible non‐parametric statistical techniques, data depth functions are gaining a growing interest in multivariate quality control. Within the literature, several notions of depth are effective for this purpose, even in the case of deviation from the normality assumption. However, the use of the Lp depth for constructing non‐parametric multivariate control charts has been surprisingly neglected so far. Hence, the goal of this work is to investigate the behavior the Lp depth in the statistical process control and to compare its performances to those of the Mahalanobis depth, which is often adopted to build depth‐based control charts. [ABSTRACT FROM AUTHOR]

We address the problem of learning the parameters of a stable linear time invariant (LTI) system with unknown latent space dimension, or order, from a single time-series of noisy input-output data. We focus on learning the best lower order approximation allowed by finite data. Motivated by subspace algorithms in systems theory, where the doubly infinite system Hankel matrix captures both order and good lower order approximations, we construct a Hankel-like matrix from noisy finite data using ordinary least squares. This circumvents the non-convexities that arise in system identification, and allows accurate estimation of the underlying LTI system. Our results rely on careful analysis of self-normalized martingale difference terms that helps bound identification error up to logarithmic factors of the lower bound. We provide a data-dependent scheme for order selection and find an accurate realization of system parameters, corresponding to that order, by an approach that is closely related to the Ho-Kalman subspace algorithm. We demonstrate that the proposed model order selection procedure is not overly conservative, i.e., for the given data length it is not possible to estimate higher order models or find higher order approximations with reasonable accuracy. [ABSTRACT FROM AUTHOR]

The bulk of social neuroscience takes a 'stimulus-brain' approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a 'brain-to-brain' approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, 'hyperscanning' setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such 'inter-brain connectivity analysis', resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses. [ABSTRACT FROM AUTHOR]

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm.

Clustered data arise frequently in many practical applications whenever units are repeatedly observed under a certain condition. One typical example for clustered data are animal experiments, where several animals share the same cage and should not be assumed to be completely independent. Standard methods for the analysis of such data are Linear Mixed Models and Generalized Estimating Equations—however, checking their assumptions is not easy, especially in scenarios with small sample sizes, highly skewed, count, and ordinal or binary data. In such situations, Wilcoxon–Mann–Whitney type effects are suitable alternatives to mean-based or other distributional approaches. Hence, no specific data distribution, symmetric or asymmetric, is required. Within this work, we will present different estimation techniques of such effects in clustered factorial designs and discuss quadratic- and multiple contrast type-testing procedures for hypotheses formulated in terms of Wilcoxon–Mann–Whitney effects. Additionally, the framework allows for the occurrence of missing data: estimation and testing hypotheses are based on all-available data instead of complete-cases. An extensive simulation study investigates the precision of the estimators and the behavior of the test procedures in terms of their type-I error control. One real world dataset exemplifies the applicability of the newly proposed procedures.

Peculiarities of metals uptake by the biofuel crop Miscanthus x giganteus were explored during plant growth at soil from the military site (Sliač, Slovakia). The experiment was carried out in greenhouse during two vegetation seasons. Research soil was predominantly elevated in Fe and Ti, while other metals (As, Cu, Mn, Sr, Zn and Zr) were presented at order of magnitude lower concentrations. No inhibition of plant growth was observed. The calculated Bioconcentration Factor showed that levels of metals’ accumulation by plant roots, stems and leaves were independent of metals’ concentrations in the soil. The accumulation of metals by stems and leaves was much lower than by roots. As, Zr, Ti were almost not accumulated by stems and leaves during both seasons; accumulation of Cu, Fe, Mn, Zn and Sr was not essential which confirmed that biomass of M. x giganteus might be processed for the energy purpose.