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An unbalanced multidimensional latent effectsbased logistic mixed model and GQL estimation for spatial binary data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200807
Brajendra C. Sutradhar; Alwell J. OyetSpatial correlation structure is the most essential tool in a spatial data analysis. However, the difficulty of modelling spatial correlations between two responses collected from two neighbouring locations is a challenge, when it is known that each of the responses may also be influenced by certain visible and/or invisible effects of other neighbouring locations. Further difficulties arise when one

Confidence intervals for data containing many zeros and ones based on empirical likelihoodtype methods J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200807
Patrick Stewart; Wei NingIn this paper, several existing datadriven nonparametric methods including empirical likelihood, adjusted empirical likelihood and transformed empirical likelihood are considered to construct confidence intervals for the mean of a population containing many zeros and ones. Meanwhile, we propose a transformed adjusted empirical likelihood which combines the merits of adjusted and transformed empirical

EM algorithms for ordered and censored system lifetime data under a proportional hazard rate model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200807
M. Hermanns; E. Cramer; H. K. T. NgIn this paper, we consider maximum likelihood estimation of the proportional parameter in a proportional hazard rate (PHR) model based on single and multiply censored order statistics, and progressively TypeII censored order statistics from lifetimes that follow the PHR model. The expectationmaximization (EM) algorithm is proposed for computing the maximum likelihood estimate (MLE) by utilizing the

Variable selection and estimation for the additive hazards model subject to lefttruncation, rightcensoring and measurement error in covariates J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200807
LiPang ChenVariable selection with censored survival data is of great practical importance, and several methods have been proposed for variable selection based on different models. However, the impacts of biased samples caused by lefttruncation and covariate measurement error to variable selection are not fully explored. In this paper, we mainly focus on the additive hazards model and analyze variable selection

Component reliability estimation based on system failuretime data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200807
Mahdi Tavangar; Majid AsadiWe consider a coherent system consists of n components with independent and identically distributed lifetimes. We use the wellknown signaturebased representation of the system reliability to estimate the reliability of components of the system when the data is the system failure times collected according to a progressively censored scheme. Different estimators for the reliability of components are

[Invited tutorial] Birnbaum–Saunders regression models: a comparative evaluation of three approaches J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200806
Alan Dasilva; Renata Dias; Victor Leiva; Carolina Marchant; Helton SauloThis study investigates three regression models based on the Birnbaum–Saunders distribution. The first model is obtained directly through the Birnbaum–Saunders distribution; the second model is obtained via a logarithmic transformation in the response variable; and the third model employs a mean parametrization of this distribution. The primary objective of this study is to compare the performance

Estimation of the expected value of the arrival epochs given a bounded period of observation for the homogeneous and nonhomogeneous Poisson processes J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200805
Hector Gomez Marquez; Adrian RamirezNafarrate; Rafael LópezBrachoIn this paper, we derive analytical expressions of the expected value of the arrival epochs given a bounded period of observation for the homogeneous Poisson process (HPP) and for the general case of a nonhomogeneous Poisson process (NHPP). These expressions are exact, but their applicability require some computational adaptations. We illustrate the results obtained through the proposed expression

Mixed effects statespace models with Studentt errors J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200729
Lina L. HernandezVelasco; Carlos A. AbantoValle; Dipak K. DeyIn this article, mixedeffects state space models (MESSM, [Liu D, Lu T, Niu XF, et al. Mixedeffects statespace models for analysis of longitudinal dynamic systems. Biometrics. 2011;67(2):476–485.]) are revisited. MESSM can be considered as an alternative to study the HIV dynamic in a longitudinal data environment, defining the mixedeffects component into statespace models setup. As in Liu et al

Two new classes of nonparametric tests for scale parameters J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200728
Manish Goyal; Narinder KumarTesting of homogeneity of populations is a very useful phenomenon in several fields. After knowing that the two populations have the same variability or not, public/private sector organizations can execute their plans accordingly. In this paper, two new classes of nonparametric tests are proposed to test whether the scale parameters of two populations are the same or not. For each class of tests, we

Information criteria in classification: new divergencebased classifiers J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200728
William D. A. Rodríguez; Getúlio J. A. Amaral; Abraão D. C. Nascimento; Jodavid A. FerreiraThe proposal of efficient classification methods is often required when common conditions (like additivity and normal stochastic behaviour) are not satisfied. Three classical classifiers are the Linear Discriminant Analysis (LDA), K Nearest Neighbours (KNN) and Quadratic Discriminant Analysis (QDA) methods. It is known that the performance of these techniques is strongly affected by the absence of

A comparison of preliminary test, Steintype and penalty estimators in gamma regression model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200727
Akram Mahmoudi; Reza Arabi Belaghi; Saumen MandalOwing to the broad applicability of gamma regression, we propose some improved estimators based on the preliminary test and Steintype strategies to estimate the unknown parameters in a gamma regression model. These estimators are considered when it is suspected that the parameters may be restricted to a subspace of the parameter space. Two penalty estimators such as LASSO and ridge regression are

Indirect inference for locally stationary ARMA processes with stable innovations J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200727
Shu Wei ChouChen; Pedro A. MorettinThe class of locally stationary processes assumes that there is a timevarying spectral representation, that is, the existence of finite second moment. We propose the αstable locally stationary process by modifying the innovations into stable distributions and the indirect inference to estimate this type of model. Due to the infinite variance, some of interesting properties such as timevarying autocorrelation

An innovative optimal randomized response model using correlated scrambling variables J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200725
Maryam Murtaza; Sarjinder Singh; Zawar HussainThis paper introduces an innovative and optimal randomized response model by making use of correlated scrambling variables for estimating the population mean of a sensitive variable. The resultant estimators are found to be unbiased. Variance expressions of the proposed estimators have also been derived. Analytical as well as empirical evidences are provided in favour of the proposed estimators relative

A nonparametric Bayesian changepoint method for recurrent events J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200721
Qing Li; Feng Guo; Inyoung KimThis paper proposes a nonparametric Bayesian approach to detect the changepoints of intensity rates in the recurrentevent context and cluster subjects by the changepoints. Recurrent events are commonly observed in medical and engineering research. The event counts are assumed to follow a nonhomogeneous Poisson process with piecewiseconstant intensity functions. We propose a Dirichlet process

Memory type ratio and product estimators for population mean for timebased surveys J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200721
Muhammad NoorulAminThe use of auxiliary variable is common at estimation stage in the form of ratio and product type estimators. All such estimators use the current sample information to estimate the population characteristics. In this study, we utilized the past samples information along with the current sample information in the form of hybrid exponentially weighted moving averages to construct memory type ratio and

New tests for exponentiality based on a characterization with random shift J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200720
J. S. Allison; Ya. Yu. Nikitin; I. A. Ragozin; L. SantanaWe derive the efficiencies of two new tests for exponentiality which are based on a recent characterization that uses the idea of a random shift. The finitesample performance of the newly proposed tests is evaluated and compared to other existing tests by means of Monte Carlo simulations. It is found that the new tests perform favourably when compared to the other tests. Overall the best performing

On a new mixturebased regression model: simulation and application to data with high censoring J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200718
Mário F. Desousa; Helton Saulo; Manoel SantosNeto; Víctor LeivaIn this paper, we derive a new continuousdiscrete mixture regression model which is useful for describing highly censored data. This mixture model employs the BirnbaumSaunders distribution for the continuous response variable of interest, whereas the Bernoulli distribution is used for the point mass of the censoring observations. We estimate the corresponding parameters with the maximum likelihood

Comparison of partial least squares with other prediction methods via generated data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200717
Atila Göktaş; Özge AkkuşThe purpose of this study is to compare the Partial Least Squares (PLS), Ridge Regression (RR) and Principal Components Regression (PCR) methods, used to fit regressors with severe multicollinearity against a dependent variable. To realize this, a great number of varying groups of datasets are generated from standard normal distribution allowing for the inclusion of different degrees of collinearities

Testing independence between two nonhomogeneous point processes in time J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200716
Ana C. Cebrián; Jesús Abaurrea; Jesús AsínPoint processes are often used to model the occurrence times of different phenomena, such as heatwaves or spike trains. Many of those problems require to study the independence between nonhommogeneous point processes in time, and this work develops three families of tests to assess that hypothesis. They can be applied to different types of processes, and all together they cover a wide range of situations

Bayesian inference for highdimensional nonstationary Gaussian processes J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200716
Mark D. Risser; Daniel TurekIn spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a personal laptop. For statisticians and data scientists who wish to conduct posterior inference and prediction with appropriate uncertainty quantification

A predictive Bayesian approach to EWMA and CUSUM charts for timebetweenevents monitoring J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200715
Sajid AliThis article introduces Bayesian predictive monitoring of timebetweenevents using Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) control charts with predictive control limits. It is shown that the proposed methodology not only overcomes the requirement of a large PhaseI data set to establish control limits, but also feasible for online process monitoring. In addition to

Optimal dynamic treatment regimes with survival endpoints: introducing DWSurv in the R package DTRreg J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200715
Gabrielle Simoneau; Erica E. M. Moodie; Michael P. Wallace; Robert W. PlattPrecision medicine is an approach to health care in which treatment decisions are tailored to patientlevel information. Statistical methods for the estimation of dynamic treatment regimes (DTRs) allow to uncover a sequence of personalized treatment rules for patients with chronic diseases. Of particular interest is the identification of an optimal DTR, that is, the sequence of treatment rules that

Parallel generalized elliptical slice sampling with adaptive regional pseudopriors J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200715
Song Li; Geoffrey K. F. Tso; Jin LiMCMC algorithm is wellknown for having difficulty exploring distant modes when the target distribution is multimodal. The reason is that a proposal state is likely to be rejected when traversing across low density regions. Focusing on this issue, we proposed parallel generalized elliptical slice sampling algorithm with adaptive regional pseudoprior (RGESS). Different from the work of Fagan et al

Maximum weighted adaptive CUSUM charts for simultaneous monitoring of process mean and variance J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200715
Abdul Haq; Faiqa RazzaqIn this paper, we propose maximum weighted adaptive Crosier CUSUM (MaxWACCUSUM) charts with three different unbiased estimators of the process mean and variance shifts for joint monitoring of the mean and variance of a normal process. The MaxWACCUSUM charts provide an overall good performance for detecting a range of joint mean and dispersion shift sizes. The run length characteristics of the proposed

Bayesian estimation of stress–strength reliability for twoparameter bathtubshaped lifetime distribution based on maximum ranked set sampling with unequal samples J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200715
Mehdi Basikhasteh; Fazlollah Lak; Mahmoud AfshariBayesian estimation and credible interval of stress–strength reliability R = P ( X < Y ) for twoparameter bathtubshaped lifetime (TPBL) distribution are considered based on simple random sampling (SRS), ranked set sampling (RSS) and maximum ranked set sampling with unequal samples (MRSSU). Monte Carlo simulations are used to compare the performances of different proposed methods for Bayesian inferences

Principal component analysis with autocorrelated data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200515
Bartolomeu Zamprogno; Valdério A. Reisen; Pascal Bondon; Higor H. Aranda Cotta; Neyval C. Reis JrThis paper contributes to the analysis, interpretation and the use of the principal component analysis in a multivariate timecorrelated linear process. The effect of ignoring the autocorrelation structure of the vector process is investigated. The results show a spurious impact of the timecorrelation on the eigenvalues. To mitigate this impact, a prefiltering procedure to whiten the data is applied

Bayesian inference for hidden truncation Pareto (IV) models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200515
Indranil GhoshIn this paper, we consider observations arising out from a hidden truncation Pareto (IV) distribution and to be used to make inferences about the inequality, precision, shape and truncation parameter(s). Two different types of dependent prior analyses are reviewed and compared with each other. We conjecture that mathematical tractability should be, perhaps, a minor consideration in choosing the appropriate

Secondorder extended particle filter with exponential family observation model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200520
Xing Zhang; Zhibin YanParticle filter is the most widely used Bayesian sequential state estimation method for nonlinear dynamic systems. When importance sampling is adopted, it is still a challenge to select an appropriate importance function for sampling to avoid particle degeneracy. This paper suggests a novel particle filter, called secondorder extended particle filter, which uses conditional normal distribution to

On enhanced estimation of population variance using unconventional measures of an auxiliary variable J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200525
Muhammad Awais Gulzar; Muhammad Abid; Hafiz Zafar Nazir; Faisal Maqbool Zahid; Muhammad RiazMost of the research work on ratio, product, and regression estimators are usually based on conventional measures such as mean, quartiles, semiinterquartile range, semiinterquartile average, coefficient of skewness, coefficient of kurtosis, etc. The efficiency of these conventional measures is doubtful in the presence of extreme values in the data. In this paper, we propose an enhanced family of

Heavy tail index estimation based on block order statistics J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200519
Li Xiong; Zuoxiang PengA new kind of heavy tail index estimator is proposed by using block order statistics in this paper. The weak consistency of the estimator is derived. The asymptotic expansion and asymptotic normality of the estimator are considered under second order regular variation conditions. Small sample simulations are presented in terms of average mean and average mean squared error to support our findings by

More powerful logrank permutation tests for twosample survival data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200601
Marc Ditzhaus; Sarah FriedrichWeighted logrank tests are a popular tool for analysing rightcensored survival data from two independent samples. Each of these tests is optimal against a certain hazard alternative, for example, the classical logrank test for proportional hazards. But which weight function should be used in practical applications? We address this question by a flexible combination idea leading to a testing procedure

Bayesian sensitivity analysis to the nonignorable missing cause of failure for hybrid censored competing risks data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200604
Fariba Azizi; Samaneh Eftekhari Mahabadi; Elham Mosayebi OmshiCompeting risks arise when an individual is exposed to the several causes of failure. In this case, the recorded data includes two components, the failure times and the cause of failure indicators. Such data may suffer from censoring in the former part and missingness in the latter part. Prior researches have ignored the missing mechanism when analysing such data which might lead to invalid statistical

Analysis of masked competing risks data with cause and time dependent masking mechanism J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200602
Hasan Misaei; Samaneh Eftekhari Mahabadi; Firoozeh HaghighiIn this paper, we discuss the estimation problem of the competing risks model when the exact cause of failure for some units may not be completely observed. The failure times of the components are assumed to follow Weibull distributions with different shape and scale parameters according to each competing risk. In the applied competing risks analysis, it is common to have failed units during life testing

The information detection for the generalized additive model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200609
SanTeng Huang; WeiYing WuMany nonlinear models such as the additive models or varying models are often used to fit the complex data. However, how to select a simplified model in the prediction problem or data interpretation is necessary and challenged. In this work, the concerned regression model consists of many unknown group regressor functions, and some of them can be irrelevant for the response variable. To find an adequate

The generalized multisample Cucconi test statistic for the location and scale parameters J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200608
Takuya Nishino; Hidetoshi MurakamiMany researchers study various test statistics for appropriately dealing with data. The nonparametric oneway analysis of variance plays an important role in biometry. For the multisample locationscale problem, the generalized Cucconi test statistic has been proposed for the location, scale, and locationscale parameters. This study derives the limiting distribution of the suggested test statistic

On the stochastic restricted Liu estimator in logistic regression model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200710
Yong Li; Yasin Asar; Jibo WuIn this paper, we study the effects of nearsingularity which is known as multicollinearity in the binary logistic regression. Furthermore, we also assume the presence of stochastic nonsample linear restrictions. The wellknown logistic Liu estimator is combined with the stochastic linear restrictions in order to propose a new method, namely, the stochastic restricted Liu estimation. Theoretical comparisons

Prediction for the processes with almost cyclostationary structure J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200710
KimHung Pho; Zhenshuai Fu; Mohammad Reza Mahmoudi; Bui Anh TuanThis study aims to establish a computational method to predict the processes with almost cyclostationary structure. The primary idea is relied on the estimating of the support of spectra and using the discrete Fourier transform and periodogram of almost cyclostationary processes. The simulation study and a real dataset are executed to test the performance of our proposed method. Our obtained results

Robust control charts for the mean of a locally linear time series J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200709
Sermad Abbas; Roland FriedWe study residual control charts for the detection of sudden changes in time series with an underlying timevarying trend. Our control charts compare the most recent observations to their direct predecessors by applying twosample tests in a moving time window to onestepahead prediction errors obtained from a local linear regression. Global model assumptions, a fixed target value, or large sets of

Penalized profile quasimaximum likelihood method of partially linear spatial autoregressive model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200707
Tizheng Li; Yue GuoIn this paper, we develop a class of penalized likelihood method to identify important explanatory variables in parametric component of partially linear spatial autoregressive model. Compared to existing estimation methods, the proposed method can simultaneously select the significant explanatory variables and estimate the nonzero parameters in the parametric component of partially linear spatial autoregressive

Robust Waldtype tests based on minimum Rényi pseudodistance estimators for the multiple linear regression model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200704
E. Castilla; N. Martín; S. Muñoz; L. PardoWe introduce a new family of Waldtype tests, based on minimum Rényi pseudodistance estimators, for testing general linear hypotheses and the variance of the residuals in the multiple regression model. The classical Wald test, based on the maximum likelihood estimator, can be seen as a particular case inside our family. Theoretical results, supported by an extensive simulation study, point out how

Variable selection of partially linear varying coefficient spatial autoregressive model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200703
Tizheng Li; Qingyan Yin; Jialong PengThe partially linear varying coefficient spatial autoregressive model is a recently proposed semiparametric spatial autoregressive model, in which some of the explanatory variables have varying coefficients while the remained explanatory variables possess constant ones. Although some estimation methods have been proposed for the partially linear varying coefficient spatial autoregressive model, the

Bayesian linear regression models with flexible error distributions J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200702
Nívea B. da Silva; Marcos O. Prates; Flávio B. GonçalvesThis work introduces a novel methodology based on finite mixtures of Studentt distributions to model the errors' distribution in linear regression models. The novelty lies on a particular hierarchical structure for the mixture distribution in which the first level models the number of modes, responsible to accommodate multimodality and skewness features, and the second level models tail behaviour

Bayesian model selection approach for coloured graphical Gaussian models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200630
Qiong Li; Xin Gao; Hélène MassamWe consider a class of coloured graphical Gaussian models obtained by imposing equality constraints on the precision matrix in a Bayesian framework. The Bayesian prior for precision matrices is given by the coloured GWishart which is the DiaconisYlvisaker conjugate. In this paper, we develop a computationally efficient model search algorithm which combines linear regression with a double reversible

Bootstrap confidence intervals for a break date in linear regressions J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200626
Seong Yeon ChangIn this article, we consider bootstrap confidence intervals, namely percentile bootstrap for obtaining confidence intervals of a break date in linear regression models. Elliott and Müller [Confidence sets for the date of a single break in linear time series regressions. J Econometrics. 1997;141:1196–1218] point out that the simulated coverage probabilities are below the nominal rate when the limiting

Approximate leavefutureout crossvalidation for Bayesian time series models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200625
PaulChristian Bürkner; Jonah Gabry; Aki VehtariOne of the common goals of time series analysis is to use the observed series to inform predictions for future observations. In the absence of any actual new data to predict, crossvalidation can be used to estimate a model's future predictive accuracy, for instance, for the purpose of model comparison or selection. Exact crossvalidation for Bayesian models is often computationally expensive, but

Projection correlation between scalar and vector variables and its use in feature screening with multiresponse data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200415
Kai Xu; Zhiling Shen; Xudong Huang; Qing ChengIn this article, we introduce a new methodology to perform feature screening for ultrahigh dimensional data with multivariate responses. Several extant screening procedures are available for multivariate responses, but they may be adversely affected by heavytailed observations or the dimension of multivariate responses. In order to attack these challenges, we first introduce a nonparametric coefficient

Bayesian analysis of the porder integervalued AR process with zeroinflated Poisson innovations J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200428
Aldo M. Garay; Francyelle L. Medina; Celso R. B. Cabral; TsungI LinIn recent years, there has been a considerable interest to study count time series with a dependence structure and appearance of excess of zeros values. Such series are commonly encountered in diverse disciplines, such as economics, financial research, environmental science, public health, among others. In this paper, we propose a stationary porder integervalued autoregressive process with zeroinflated

Properties and approximate pvalue calculation of the Cramer test J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200424
Alison Telford; Charles C. Taylor; Henry M. Wood; Arief GusnantoTwosample tests are probably the most commonly used tests in statistics. These tests generally address one aspect of the samples' distribution, such as mean or variance. When the null hypothesis is that two distributions are equal, the Anderson–Darling (AD) test, which is developed from the Cramer–von Mises (CvM) test, is generally employed. Unfortunately, we find that the AD test often fails to identify

A new estimator of the selfsimilarity exponent through the empirical likelihood ratio test J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200504
Sergio Bianchi; Qiushi LiA new method is proposed to estimate the selfsimilarity exponent. Instead of applying finite moment(s) methods, a goodnessoffit statistic is designed to test whether two rescaled sequences are drawn from the same distribution, which is the definition of selfsimilarity. The test is the empirical likelihood ratio, which is robust with respect to processes with dependence. We provide a closed formula

Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200512
Aristidis K. NikoloulopoulosAvailable extensions of generalized estimating equations for longitudinal ordinal response require a conversion of the ordinal response to a vector of binary category indicators. That leads to a rather complicated working correlation structure and to large matrices when the number of categories and dimension of the clusters are large. Weighted scores estimating equations are constructed to overcome

An optimal systematic sampling scheme J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200507
Zaheen Khan; Javid Shabbir; Sat Gupta; Amjad ShamimThis paper aims to increase the efficiency of estimator through the optimal pairing of units in systematic sampling. The proposed Optimal Systematic Sampling Scheme is very simple, yet more generalized than any other systematic sampling scheme available in the literature. This scheme is applicable not only for the case where population size is multiple of sample size but also for other cases where

Simultaneous confidence intervals for the quantile differences of several twoparameter exponential distributions under the progressive type II censoring scheme J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200519
Ahad Malekzadeh; Mahmood KharratiKopaeiWe consider the problem of constructing simultaneous confidence intervals (SCIs) for the pairwise quantile differences of several heterogeneous twoparameter exponential distributions when the data are censored under a progressive Type II censoring scheme. We propose three simulationbased SCIs and a classic set of SCIs for pairwise quantile differences when a fixed value of a percentage is given.

A new method for sequential learning of states and parameters for statespace models: the particle swarm learning optimization J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200514
I. R. E. Guzman; P. C. C. CcoriAccuracy of parameter estimation and efficiency of state simulation are common concerns in the implementation of statespace models. Even widely used methods such as Kalman filters with MCMC and Particle Filter, still present concerns with efficiency and accuracy, despite their successful results in their respective applications.This article presents a new method combining the structure of particle

On hypothesis testing inference in locationscale models under model misspecification J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200512
Francisco F. Queiroz; Artur J. LemonteThe likelihood ratio, Wald, score and gradient test statistics can result in misleading conclusions when the assumed parametric model to the data at hand is not correctly specified. To overcome this issue, robust versions of these test statistics have been proposed in the statistic literature under model misspecification. In this paper, we address the issue of performing hypothesis testing inference

A new approach of subgroup identification for highdimensional longitudinal data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200515
Mu Yue; Lei HuangDiscovering a medication that suitable for all patients is not possible due to the fact that the reaction to medication may differ significantly across different patient subgroups. The heterogeneity of treatment effects is central to the agenda for both personalized medicine and treatment selection. To expedite the development of tailored therapies and improve the treatment efficacy, identification

Nonparametric probability density functions of entropy estimators applied to testing the Rayleigh distribution J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200624
Hadi Alizadeh Noughabi; Jalil JarrahiferizThe 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 some new test statistics for examining the Rayleigh goodness of fit based on correcting moments of nonparametric probability density functions of entropy estimators. Critical points and power of the tests are

Feature screening of quadratic inference functions for ultrahigh dimensional longitudinal data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200623
Peng Lai; Weijuan Liang; Fangjian Wang; Qingzhao ZhangThis paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the withinsubject correlation into consideration and is more efficient and robust than some parametric model assumptions for the working covariance matrix in each

Robust Waldtype test statistics based on minimum Cdivergence estimators J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200623
Avijit Maji; Leandro PardoMaji et al. [Robust statistical inference based on the Cdivergence family. Ann Inst Stat Math. 2019;71:1289–1322] introduced the minimum Cdivergence estimators and plugging them in the Cdivergence measures give test statistics for testing simple null and composite null hypotheses. One inconvenience of these test statistics is that their asymptotic distribution is not, in general, a chisquare distribution

A ridge to homogeneity for linear models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200619
Stanislav AnatolyevIn some heavily parameterized models, one may benefit from shifting some of parameters towards a common target. We consider L 2 shrinkage towards an equal parameter value that balances between unrestricted estimation (i.e. allowing full heterogeneity) and estimation under equality restriction (i.e. imposing full homogeneity). The penalty parameter of such ridge regression estimator is tuned using leaveoneout

A simple test for the difference of means in metaanalysis when studyspecific variances are unreported J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20200619
Patarawan Sangnawakij; Dankmar BöhningStandard metaanalysis requires the quantity of interest and its estimated variance to be reported for each study. Datasets that lack such variance information pose important challenges to metaanalytic inference. In a study with continuous outcomes, only sample means and sample sizes may be reported in the treatment arm. Classical metaanalytical technique is unable to apply statistical inference