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There are many alternative approaches to selecting mortality models and forecasting mortality. The standard practice is to produce forecasts using a single model such as the Lee-Carter, the Cairns-Blake-Dowd, or the Age- Period-Cohort model, with model selection based on in-sample goodness of fit measures. However, increasingly cross-validation measures based on forecasts are used in mortality model selection, and model combination methods such as Bayesian Model Averaging and Model Confidence Set have been proposed as alternatives to using a single model. We present a stacked regression ensemble method that optimally combines different mortality models to reduce the mean squared errors of mortality rate forecasts and mitigate model selection risk. Stacked regression uses a supervised machine learning algorithm to approximate the horizon-specific weights by minimizing the cross-validation criterion for each forecasting horizon. The horizon-specific weights facilitate the development of a mortality model combination customized to each horizon. Unlike other model combination methods, stacked regression simultaneously solves model selection and estimates model combinations to improve model forecasts. We use 44 populations from the Human Mortality Database to compare the stacked regression mortality approach with other alternative methods. We show that using one-year-ahead to 15−year-ahead out-of-sample mean squared errors, the stacked regression ensemble approach improves mortality forecast accuracy by 13% - 49% and 19% - 90% for females over individual mortality models. The stacked regression ensemble has better predictive accuracy than other model combination methods, including Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. We provide a user-friendly open-source R package, CoMoMo, that estimates models and provides mortality rate forecasts for different mortality model combination methods.

Author(s): Zheng, Bang Quan | Advisor(s): Bentler, Peter M | Abstract: Does Reweighted Least Squares (RLS) perform better in small samples than maximum likelihood (ML) for mean and covariance structure? ML statistics in covariance structure analysis are based on the asymptotic normality assumption; however, actual applications of structural equation modeling (SEM) in social and behavioral science research usually involve small samples. It has been found that chi-square tests often incorrectly over-reject the null hypothesis: Σ=Σ(θ), because when sample is small the sample covariance matrix becomes ill-conditioned and entails unstable estimates. In certain SEM models, the vector of parameter must contain both means, variances and covariances. Yet, whether RLS also works in mean and covariance structure remains unexamined. This research is an extended examination of reweighted least squares in mean and covariance structure. Specifically, we replace biased covariance matrix in traditional GLS function (Browne, 1974) with the unbiased sample covariance matrix that derives from ML estimation. Moreover, under the assumption of multivariate normality, a Monte Carlo simulation study was carried out to examine the statistical performance as compared with ML methods in different sample sizes. Based on empirical rejection frequencies and empirical averages of test statistic, this study shows that RLS performs much better than ML in mean and covariance structure models when sample sizes are small.

In a multiple regression model setup, we examine the effects of trimming and winsorization of regressors on the regression estimates and their efficiency. The exact results developed here to enable...

Bennett (Statistics in Medicine 4 (1985), 535-539) proposed a goodness-of-fit statistic to compare two predictive values in an attempt to improve upon its earlier version he derived in 1976. Howeve...

This study, conducted between October 2018 and May 2019, aimed to develop an Exercise Attitude Scale in Turkish (EAS-Turkish) for pregnant women and to determine its validity and reliability. The scale was prepared in Turkish to comprehensively measure pregnant women's exercise attitudes. Then, it administered in 253 pregnant women, who were native speakers of Turkish and older than 18 years, in the Obstetrics Polyclinic at Ataturk Training and Research Hospital, Ankara, Turkey. Validity with exploratory and confirmatory factor analysis and reliability with test-retest and internal consistency methods were tested. The 37-item scale was found to be 2-dimensional (knowledge and benefit; barrier). All indexes of the goodness of fit (χ2/df = 2.0, Comparative Fit Index = 0.90, Goodness-of-fit Index = 0.85, Root Mean Square Error of Approximation = 0.06) indicated that the fit between the model and the sample data was acceptable. The item-total score correlations varied between r = 0.22 and 0.60. The Cronbach alpha coefficients were found as 0.90 for the whole scale, 0.91 for the knowledge and benefit sub-dimension, and 0.87 for the barrier sub-dimension. In the test-retest analysis conducted with 41 pregnant women, the reliability coefficients were detected as 0.93 for the whole scale and 0.84 for each sub-dimension. The EAS-Turkish for pregnant women was found to be a valid and reliable tool.

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%.

Multilevel modeling is a modern approach to deal with hierarchical or a nested data structure which can assess the variability between clusters. Bayesian Markov Chain Monte Carlo (MCMC) methods of ...

In this article, we propose new semiparametric and bootstrap tests based on the ratio of two variances under a density ratio model and compare them with existing tests for testing the equality of t...

Normal distribution is commonly assumed distribution in statistical inference. Therefore, goodness-of-fit test for normality is required as a preliminary procedure in the applications. Most relevan...

The common used goodeness-of-fit tests are based on the empirical distributions functions (EDF) where distances between empirical and theoretical hypothesized distributions are compared to critical...

This study considers a goodness of fit test based on the quadratic distance (QD) in composite hypotheses. Lindsay et al. [Quadratic distances on probabilities: a unified approach. Ann Statist. 2008...

A two-stage data-driven Spectrum Estimation (SE) technique has been introduced for a Cognitive Radio (CR) system involving Null-Hypothesis approach using robust Chi-Square Goodness of Fit (GoF) for...

Mean time to failure (MTTF) function plays an important role in connection with age replacement policy. In this paper, a goodness of fit test is proposed for MTTF function utilizing a weighted inte...

Some goodness-of-fit tests for interval censored data are proposed. For this aim, we introduce a divergence measure based on Kullback–Leibler divergence and use a resampling method called the lever...

Ya-Ting Yang,1,2 Han-Yun Chang,1 Chung-Yao Hsu,3 Jamie M Zeitzer2,4 1Institute of Education, National Cheng Kung University, Tainan City, Taiwan; 2Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; 3Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; 4Mental Illness Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA, USACorrespondence: Jamie M Zeitzer Email jzeitzer@stanford.eduPurpose: The Cleveland Adolescent Sleepiness Questionnaire was originally developed and published in English and has served as a valid and effective tool for the assessment of adolescents’ experiences with sleepiness in a variety of situations. To allow for comparisons between sleepiness in adolescents from different cultures, and with different linguistic backgrounds, reliable and valid measurement tools are necessary. The purpose of this study was to translate and validate a Chinese version of the Cleveland Adolescent Sleepiness Questionnaire (C-CASQ).Materials and Methods: Sensitivity, specificity, internal consistency, and criterion validity data for the C-CASQ were tested using 458 adolescents in Taiwan. Data from 191 participants were used to establish internal consistency reliability and conduct exploratory factor analysis (EFA), while data from 267 participants were used to establish criterion validity and conduct confirmatory factor analysis (CFA). Initial criterion validity was established through a comparison of the C-CASQ with scores from the Chinese version of the Morningness-Eveningness Scale for Children, a measure of chronotype.Results: EFA resulted in four factors, consistent with the original English version of the CASQ, while CFA established goodness of fit. The scale demonstrated acceptable to good internal consistency (α = 0.77∼ 0.86). Initial criterion validity was evident as the total score and each of the subscale scores on the C-CASQ was significantly higher (greater sleepiness) in evening-types.Conclusion: The C-CASQ appears to be a psychometrically sound measure to evaluate sleepiness in Chinese-speaking adolescents.Keywords: teens, sleep, alertness, chronotype

The weighted forms of generalized survival and failure entropies of order ($\alpha,\beta$) are proposed and some properties are obtained. We further propose the dynamic versions of weighted generalized survival and failures entropies and obtained some properties and bounds. Characterization for Rayleigh and power distributions are done by dynamic weighted generalized entropies. We further consider the empirical versions of generalized weighted survival and failure entropies and using the difference between theoretical and empirical survival entropies a test for exponentiality is considered., Comment: 15 pages

This article focuses on the idea of recognizing distributions rather than performing classic goodness-of-fit tests (GoFTs). In order to recognize distributions, the method of potential functions (M...

The geometric distribution is one of the most widely used count distributions. Novel goodness of fit tests for this distribution are suggested taking advantage of a characterization of that distrib...

Modeling real life data is often a demanding task and plethora of distributional models have been proposed in the statistical literature in an attempt to describe different data sets in a better wa...

The logit binomial logistic dose response model is commonly used in applied research to model binary outcomes as a function of the dose or concentration of a substance. This model is easily tailored to assess the relative potency of two substances. Consequently, in instances where two such dose response curves are parallel so one substance can be viewed as a dilution of the other, the degree of that dilution is captured in the relative potency model parameter. It is incumbent that experimental researchers working in fields including biomedicine, environmental science, toxicology and applied sciences choose efficient experimental designs to run their studies to both fit their dose response curves and to garner important information regarding drug or substance potency. This article provides far-reaching practical design strategies for dose response model fitting and estimation of relative potency using key illustrations. These results are subsequently extended here to handle situations where the assessment of parallelism and the proper dose-scale are also of interest. Conclusions and recommended strategies are supported by both theoretical and simulation results.

Multilevel modelling is a novel approach to analyse data which consist of a hierarchical or a nested structure. With advancements in multilevel modelling, there has been an advancement in the estim...

In this article, we propose several statistics to conduct goodness-of-fit tests for Rayleigh distribution based on progressively Type-II censored data, where a cumulative entropy and its upper and ...

The lack of closed representations for the density functions of the α-stable distributions, when considering Bayesian inference using Markov Chain Monte Carlo methods, has historically lead to the ...

Chi-squared tests for lack of fit are traditionally employed to find evidence against a hypothesized model, with the model accepted if the Karl Pearson statistic comparing observed and expected numbers of observations falling within cells is not significantly large. However, if one really wants evidence for goodness of fit, it is better to adopt an equivalence testing approach in which small values of the chi-squared statistic are evidence for the desired model. This method requires one to define what is meant by equivalence to the desired model, and guidelines are proposed. Then a simple extension of the classical normalizing transformation for the non-central chi-squared distribution places these values on a simple to interpret calibration scale for evidence. It is shown that the evidence can distinguish between normal and nearby models, as well between the Poisson and over-dispersed models. Applications to evaluation of random number generators and to uniformity of the digits of pi are included. Sample sizes required to obtain a desired expected evidence for goodness of fit are also provided., 25 pages, 6 figures, 4 tables this is a substantial revision over the first arkived version. The published version (see below) also includes R code and more examples as supplementary material

This study evaluated the psychometric properties of the Scale of Fear of COVID-19 (FCV-19S) in a sample of 1,291 Argentines. The two-related factor structure of the FCV-19S had satisfactory goodness-of-fit indices using structural equation modeling and item response theory. Further results showed that the reliability was adequate, the factor structure was strictly invariable across age groups, and the model that evaluated the relationships between fear of COVID-19, anxiety, and depression had adequate goodness of fit indices as well. The results indicated that FCV-19S has strong psychometric properties to measure fear of COVID-19 in the general population of Argentina. Fil: Caycho Rodríguez, Tomás. Universidad Privada del Norte; Perú Fil: Vilca, Lindsey W.. Universidad Peruana Unión; Perú Fil: Cervigni, Mauricio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto Rosario de Investigaciones en Ciencias de la Educación. Universidad Nacional de Rosario. Instituto Rosario de Investigaciones en Ciencias de la Educación; Argentina Fil: Gallegos de San Vicente, Miguel Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto Rosario de Investigaciones en Ciencias de la Educación. Universidad Nacional de Rosario. Instituto Rosario de Investigaciones en Ciencias de la Educación; Argentina Fil: Martino, Pablo. Universidad Nacional de Rosario; Argentina Fil: Portillo, Nelson. Boston College; Estados Unidos Fil: Barés, Ignacio. Universidad Nacional de Rosario; Argentina Fil: Calandra, Manuel Santiago. Universidad Nacional de Rosario; Argentina Fil: Burgos Videla, Carmen. Universidad de Atacama.; Chile

Permutation tests are highly versatile non-parametric procedures that can be used to address a wide set of statistical problems, without strict assumptions on data distribution. The Non-Parametric ...

Mixed responses within clusters are becoming an increasingly popular phenomenon. However, this scenario is a complex one. This research is based on survival and count response data which occur freq...

There have been numerous tests proposed to determine whether or not the exponential model is suitable for a given data set. In this article, we propose a new test statistic based on spacings to test whether the general progressive Type-II censored samples are from exponential distribution. The null distribution of the test statistic is discussed and it could be approximated by the standard normal distribution. Meanwhile, we propose an approximate method for calculating the expectation and variance of samples under null hypothesis and corresponding power function is also given. Then, a simulation study is conducted. We calculate the approximation of the power based on normality and compare the results with those obtained by Monte Carlo simulation under different alternatives with distinct types of hazard function. Results of simulation study disclose that the power properties of this statistic by using Monte Carlo simulation are better for the alternatives with monotone increasing hazard function, and otherwise, normal approximation simulation results are relatively better. Finally, two illustrative examples are presented.

The stochastic block model is widely used for detecting community structures in network data. How to test the goodness-of-fit of the model is one of the fundamental problems and has gained growing ...

This study proposes two new methods of goodness-of-fit (GOF) tests for progressively Type-II censored data from any continuous distribution. For the first method, we transform the original censored...

This article puts forward an idea of recognizing distributions rather than carrying-out classic goodness-of-fit tests (GoFTs). For the purpose of recognizing the k-nearest neighbors (kNN) rule is a...

Informally testing the fit of a probability distribution model is educationally a desirable precursor to formal methods for senior secondary school students. Limited research on how to teach such an informal approach, lack of statistically sound criteria to enable drawing of conclusions, as well as New Zealand assessment requirements led to this study. Focusing on the Poisson distribution, the criteria used by ten Grade 12 teachers for informally testing the fit of a probability distribution model was investigated using an online task-based interview procedure. It was found that criteria currently used by the teachers were unreliable as they could not correctly assess model fit, in particular, sample size was not taken into account. The teachers then used an interactive goodness of fit simulation-based visual inference tool (GFVIT) developed by the first author to determine if the teachers developed any new understandings about goodness of fit. After using GFVIT teachers reported a deeper understanding of model fit and that the tool had allowed them to take into account sample size when testing the fit of the probability distribution model through the visualization of expected distributional shape variation. Hence, a new informal test for the fit of a probability distribution is proposed.

This study investigates the role of ports in the dynamics of the city size distribution in a system of cities based on their geospatial proximity. We used an interdisciplinary method based on the c...

The purpose of this two-wave longitudinal study was to examine the associations between work engagement and workaholism to better understand the psychological mechanisms underpinning high levels of...

Objective This study aimed to compare the quality of life (QoL) in patients with type 2 diabetes using two WHOQoL -BREF and SF-36 questionnaires in Iran. Patients and methods In this cross-sectional study, 1847 patients with type 2 diabetes were recruited from rural health-care centers affiliated to Neyshabur County (Iran) in 2012. In addition to demographic information, two questionnaires WHOQoL-BREF and SF-36 questionnaires were used for data collection. Cronbach's alpha coefficient was used for scale reliability. We conducted an exploratory factor analysis for the investigation of construct validity. Convergent and discriminant validity were analyzed using Spearman correlation coefficient. To determine the relationships between the eight domains of SF-36 and four domains of the WHOQoL-BREF, structural equation modelling was performed. Results Cronbach's alpha coefficients were acceptable for all domains of both WHOQoL-BREF (0.69-0.86) and SF-36 (0.63 -0.92) questionnaires. The principal component analysis showed two separate factors: one for all domains of SF-36 and another for all domains of WHOQoL-BREF. Spearman correlation coefficients of both instruments were partly to strongly correlated with most domains (r ≥0.40). Correlations for domains with similar constructs were stronger than those measuring varied constructs. Structural equation modelling recommended approximately moderate relationships among the SF-36 and WHOQoL-BREF domains. Conclusion Our study suggests that SF-36 and WHOQoL-BREF are reliable instruments for clinical and research uses, respectably. However, results of the goodness of fit showed that the WHOQoL-BREF was fitted well. Also, the WHOQoL-BREF can be considered more suitable for the study population.

Multilevel structural equation models (MSEM) are typically evaluated on the basis of goodness of fit indices. A problem with these indices is that they pertain to the entire model, reflecting simul...

We used simulation techniques to assess the item-level and familywise Type I error control and power of an IRT item-fit statistic, the S-X2 . Previous research indicated that the S-X2 has good Type...

Scoring procedures for many rater-mediated performance assessments include score resolution procedures in which a third rater adjudicates discrepancies between two raters’ ratings of the same perfo...

For many years, authors have been interested in developing methods for generating distributions that provide a flexible family to model lifetime variables. This paper proposes an exact inference ap...

Rater fit analyses provide insight into the degree to which rater judgments correspond to expected properties, as defined within a measurement framework. Parametric models such as the Rasch model p...

This study examines and compares four person-fit statistics (PFSs) in the framework of the D-scoring method (DSM): (a) van der Flier’s U3 statistic; (b) Ud statistic, as a modification of U3 under ...

The Rayleigh distribution is widely used to model right skewed data and therefore it is important to develop efficient goodness of fit tests for this distribution. In this article, we introduce som...

Residual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov mode...

In this paper, we had constructed the goodness-of-fit tests incorporating several components, like expectation and covariance function, for identification of a non-centered stationary Gaussian stoc...

In Pharmacokinetic (PK) and Pharmacodynamic (PD), the hazard rate functions play a central role in modelling time-to-event data. In the context of assessing the appropriateness of a given parametri...

Data simulation and Monte Carlo simulation studies are important skills for researchers and practitioners of educational and psychological measurement, but there are few resources on the topic spec...

Cognitive diagnosis models (CDMs) have gained increasing popularity recently because of their potential to provide diagnostic inferences that can inform learning and teaching. However, the developm...

In the present work, we consider parameter estimation and hypothesis testing based on a general class of measures, namely the (Φ,α)-power divergence family for multinomial populations. In particula...

In this work, we study semiparametric copula models under bivariate censoring. Basing on divergences theory, we propose new estimates for the parameter of the considered model and we establish thei...