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Bayesian sensitivity analysis to the nonignorable missing cause of failure for hybrid censored competing risks data J. Stat. Comput. Simul. (IF 0.767) 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 0.767) 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

More powerful logrank permutation tests for twosample survival data J. Stat. Comput. Simul. (IF 0.767) 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

On enhanced estimation of population variance using unconventional measures of an auxiliary variable J. Stat. Comput. Simul. (IF 0.767) 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

Secondorder extended particle filter with exponential family observation model J. Stat. Comput. Simul. (IF 0.767) 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

Simultaneous confidence intervals for the quantile differences of several twoparameter exponential distributions under the progressive type II censoring scheme J. Stat. Comput. Simul. (IF 0.767) 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.

Heavy tail index estimation based on block order statistics J. Stat. Comput. Simul. (IF 0.767) 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

Principal component analysis with autocorrelated data J. Stat. Comput. Simul. (IF 0.767) 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

A new approach of subgroup identification for highdimensional longitudinal data J. Stat. Comput. Simul. (IF 0.767) 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

Bayesian inference for hidden truncation Pareto (IV) models J. Stat. Comput. Simul. (IF 0.767) 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

A new method for sequential learning of states and parameters for statespace models: the particle swarm learning optimization J. Stat. Comput. Simul. (IF 0.767) 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

Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response J. Stat. Comput. Simul. (IF 0.767) 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

On hypothesis testing inference in locationscale models under model misspecification J. Stat. Comput. Simul. (IF 0.767) 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

An optimal systematic sampling scheme J. Stat. Comput. Simul. (IF 0.767) 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

A new estimator of the selfsimilarity exponent through the empirical likelihood ratio test J. Stat. Comput. Simul. (IF 0.767) 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

Bayesian analysis of the porder integervalued AR process with zeroinflated Poisson innovations J. Stat. Comput. Simul. (IF 0.767) 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

Bayesian nonparametric estimation of bandwidth using mixtures of kernel estimators for lengthbiased data J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200428
S. Rahnamay Kordasiabi; S. KhazaeiKernel density estimation has been applied in many computational subjects. In this paper, we propose a density estimation procedure from a Bayesian nonparametric perspective using Dirichlet process prior for the lengthbiased data under an unknown kernel function. In this situation, the kernel within the Dirichlet process mixture model will be approximated by the kernel density estimator. We present

Properties and approximate pvalue calculation of the Cramer test J. Stat. Comput. Simul. (IF 0.767) 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

European call price modelling using neural networks in considering volatility as stochastic with comparison to the Heston model J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200423
Yacin Jerbi; Samira ChaabeneThe aim of this paper is to model the European call price using neural networks (NNs). Many existing works have treated the problem as Hutchinson et al. [(1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. J Finance. 49(3):851–889] Compared to these previous studies, the originality of our work consists in considering the volatility as stochastic and

The monitoring of mean vectors with VCS charts for multivariate processes J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200415
Antonio Fernando Branco Costa; Alexandre Fonseca Torres; Pedro Paulo BalestrassiWe propose to control the mean vector by taking larger samples but inspecting fewer quality characteristics if we work with samples of size n to control bivariate processes, then 2n observations are usually collected, half are observations of X1 and the other half are observations of X2; alternatively, we might work with samples of size 2n if only observations of X1 (or X2) are collected; in both cases

Projection correlation between scalar and vector variables and its use in feature screening with multiresponse data J. Stat. Comput. Simul. (IF 0.767) 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 inference for mixtures of von Mises distributions using reversible jump MCMC sampler J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200415
Kees Mulder; Pieter Jongsma; Irene KlugkistCircular data are encountered in a variety of fields. A dataset on music listening behaviour throughout the day motivates development of models for multimodal circular data where the number of modes is not known a priori. To fit a mixture model with an unknown number of modes, the reversible jump MetropolisHastings MCMC algorithm is adapted for circular data and presented. The performance of this

Logarithmic calibration for partial linear models with multiplicative distortion measurement errors J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200409
Jun Zhang; Yiping Yang; Sanying Feng; Zhenghong WeiIn this paper, we propose a new identifiability condition by using the logarithmic calibration for the multiplicative distortion partial linear measurement errors models, when neither the response variable nor the covariates in the parametric part can be directly observed. We propose a logarithmic calibration estimation procedure for the unobserved variables. Then, a profile least squares estimator

The EBayesian estimation and its EMSE of Pareto distribution parameter under different loss functions J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200409
Ming HanThis paper studies the EBayesian estimations and their EMSE of Pareto distribution parameter under different loss functions. In order to measure the estimated error, in the case of the two hyper parameters, the definition of EMSE (expected mean square error) is introduced based on the definition of EBayesian estimation, and the formulas of EBayesian estimation and the formulas of EMSE are given

A data transformation to deal with constant under/overdispersion in Poisson and binomial regression models J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200407
Luis Hernando Vanegas; Luz Marina RondonThis paper proposes a data transformation to deal with the presence of constant under/overdispersion relative to the Poisson or binomial assumptions. The proposed methodology is very simple as it does not require to replace the Poisson or binomial by more complex regression models based on more flexible distributions. The new approach consist of a transformation of the response variable, followed

Estimation of error correction model with measurement errors J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200403
Hanwoom Hong; Sung K. Ahn; Sinsup ChoEffects of measurement errors on the analysis of error correction models (ECMs) of vector processes observed with measurement errors were studied in Hong et al. (2016. Analysis of cointegrated models with measurement errors. Journal of Statistical Computation and Simulation. 2016;86:623–639). It was found that statistically undesirable effects on the analysis attributable to endogeneity in the ECM

Statistical inference for multinomial populations based on a double index family of test statistics J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200402
Christos Meselidis; Alex KaragrigoriouIn 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 particular, we propose a general family of double index (Φ,α)test statistics that involves two indices, the values of which play a key role in the effectiveness and accuracy of the proposed methodology. The asymptotic

A study of the data augmentation strategy for stochastic differential equations J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200402
Ge Liu; Peter F. Craigmile; Radu HerbeiMultivariate stochastic differential equations (SDEs) are commonly used in many applications. Statistical inference based on discretely observed data requires estimating the transition density, which is unknown for most models. Typically, one would estimate the transition density and use the approximation for statistical inference. However, many estimation methods will fail when the observations are

Realtime estimation for functional stochastic regression models J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200402
Amir Aboubacar; Mohamed ChaouchIn this paper, a heteroscedastic functional regression model with martingale difference errors is considered. We are interested in realtime estimation of the regression as well as the conditional variance operators when the response is a realvalued random variable and the covariate belongs to an infinitedimensional space. A Robbins–Monrotype estimator of the conditional variance is introduced when

Reducedbias and partially reducedbias meanoforderp valueatrisk estimation: a MonteCarlo comparison and an application J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200331
M. Ivette Gomes; Frederico Caeiro; Fernanda Figueiredo; Lígia HenriquesRodrigues; Dinis PestanaOn the basis of a sample of either independent, identically distributed or possibly weakly dependent and stationary random variables from an unknown model F with a heavy righttail function, and for any small level q, the valueatrisk (VaR) at the level q, i.e. the size of the loss that occurs with a probability q, is estimated by new semiparametric reducedbias procedures based on the meanoforderp

A control chart to monitor the process mean based on inspecting attributes using control limits of the traditional Xbar chart J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200327
R. C. Quinino; L. L. Ho; F. R. B. Cruz; L. F. BessegatoThis paper proposes a new control chart, denoted by X¯tn, for evaluating the stability of a process mean, which is based on attribute inspection. In the X¯tn control chart, the mean of the quality characteristic of interest is controlled by a gonogo gauge device that generates five categorizations. In equallyspaced times, samples of n items are collected, their categories are determined, and random

Improved estimation in elliptical linear mixed measurement error models J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200324
Hadi EmamiIn this paper, we propose a set of improved estimators for the fixed effect parameters in the linear mixed models when the covariates are measured with additive errors and prior information for the parameters is available. When it is suspected that the parameter vector may be the nullvector with some degree of uncertainty, we define improved estimators which including the preliminary test estimator

Robust estimation approach for spatial error model J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200323
Vural Yildirim; Yeliz Mert KantarSpatial regression models, used to model spatial relationships, have received considerable attention in recent years since numerous data sets have been collected with geographical references in space. The spatial error model (SEM), among spatial regression models, has been widely applied for spatial data in the literature due to its simple structure. However, it is known that the classical estimation

Longtailed graphical model and frequentist inference of the model parameters for biological networks J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200313
Melih Ağraz; Vilda PurutçuoğluThe biological organism is a complex structure regulated by interactions of genes and proteins. Various linear and nonlinear models can define activations of these interactions. In this study, we have aimed to improve the Gaussian graphical model (GGM), which is one of the wellknown probabilistic and parametric models describing steadystate activations of biological systems, and its inference based

An empirical threshold of selection probability for analysis of highdimensional correlated data J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200311
Kipoong Kim; Jajoon Koo; Hokeun SunFor the analysis of highdimensional data, regularization methods based on penalized likelihood have been extensively studied over the last few decades. But, they commonly require the optimal choice of tuning parameters to select relevant variables. Although crossvalidation has been popularly used for tuning parameter selection, its selection result is not often stable due to random split of samples

Cumulative/dynamic ROC curve estimation under interval censorship J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200310
Susana DíazCoto; Pablo MartínezCamblor; Norberto Octavio CorralBlancoThe receiver operating characteristic (ROC) curve is a graphical tool commonly used to assess the discriminatory ability of continuous markers in binary classification problems. Different extensions of the ROC curve have been proposed in the prognosis context, where the characteristics in the study are timedependent events. Perhaps the most direct generalization is the socalled cumulative/dynamic

FDA: theoretical and practical efficiency of the local linear estimation based on the kNN smoothing of the conditional distribution when there are missing data J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200310
Mustapha Rachdi; Ali Laksaci; Ibrahim M. Almanjahie; Zouaoui ChikrElmezouarWe aim to estimate effectively the conditional distribution function (CDF) of a scalar response variable, with missing data at random, given a functional covariable. For this aim, we combine the local linear approach with the kernel nearest neighbours procedure to construct a new estimator of the CDF. A fundamental issue of interest is to study the impact of the missing observations on the performances

Compromise allocation problem in multivariate stratified sampling with flexible fuzzy goals J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200306
Ahteshamul Haq; Irfan Ali; Rahul VarshneyIn a multivariate stratified sample survey, we assumed pcharacteristics which are to be measured on each unit of the population and the population is further subdivided into L subpopulations. For estimating the ppopulation means of all characteristics, which are not known in advance usually, a random sample is taken out from the population with the help of simple random sampling. In a multivariate

Sequential change point detection in ARMAGARCH models J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200304
Junmo Song; Jiwon KangThis study investigates a sequential procedure to detect changes in the parameter of ARMAGARCH models. Following the test procedure by Berkes et al. [Sequential changepoint detection in GARCH(p,q) models. Econ Theory. 2004;20:1140–1167], we introduce a stopping time for monitoring procedure based on quasilikelihood score function of ARMAGARCH model. The asymptotic properties of the monitoring procedure

A modified information criterion for tuning parameter selection in 1d fused LASSO for inference on multiple change points J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200303
J. Lee; J. ChenInference about multiple change points has been an interesting topic in the statistics literature. Recently, the high throughput technologies became the most popularly used tools in genomic studies and yielded massive data. In particular, when the concern is searching for heterogenous segments in a massive data set, it becomes an interesting problem in statistical change point analysis. That is, one

An ABC approach for CAViaR models with asymmetric kernels J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200303
Georgios TsiotasThe Value at Risk (VaR) is a risk measure that is widely used by financial institutions to allocate risk. Optimal conditional VaR estimates are typically generated using a likelihood function based on the checkloss function. However, issues such as VaR's bias estimation and asymmetric financial decisionmaking, based on the sign of the forecast error, can result in the use of combined losses or of

An adaptive variableparameters scheme for the simultaneous monitoring of the mean and variability of an autocorrelated multivariate normal process J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200227
Hamed Sabahno; Philippe Castagliola; Amirhossein AmiriDue to advances in technology, sampling procedures and short lag times between successive sampling, autocorrelation among the measured data has become common in most applications. Neglecting autocorrelation leads to a poor false alarm performance. In the current paper, the effect of the autocorrelation on the performance of a variableparameters multivariate single control chart is investigated in

Efficient sparse portfolios based on composite quantile regression for highdimensional index tracking J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200224
Ning LiIndex tracking is a wellknown passive management strategy that seeks to match the performance of a benchmark index. To the best of our knowledge, the existing literatures for sparse index tracking are mainly focus on the penalized least squares (LS) regression under the noshort selling constraint. In this paper, we propose an efficient sparse portfolio that based on composite quantile regression

Sufficient dimension folding via tensor inverse regression J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200220
Xiangjie Li; Jingxiao ZhangSufficient dimension reduction (SDR) techniques have proven to be very useful data analysis tools in various applications. Conventional SDR methods mainly tackle simple vectorvalued predictors, but they are inappropriate for data with array (tensor)valued predictors. In this paper, we propose a tensor dimension reduction approach based on inverse regression, and we refer to it as TIRE, which reduces

Fast deflation sparse principal component analysis via subspace projections J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200216
Cong Xu; Min Yang; Jin ZhangThe implementation of conventional sparse principal component analysis (SPCA) on highdimensional data sets has become a time consuming work. In this paper, a series of subspace projections are constructed efficiently by using Householder QR factorization. With the aid of these subspace projections, a fast deflation method, called SPCASP, is developed for SPCA. This method keeps a good tradeoff between

Generalized skewness correction structure of X̄ control chart for unknown process parameters and skewed probability distributions J. Stat. Comput. Simul. (IF 0.767) Pub Date : 20200213
Rashid Mehmood; Muhammad Hisyam Lee; Ambreen Afzel; Sheraz Bashir; Muhammad RiazIn this article, we have highlighted limitations of existing structures of X¯ control chart for unknown parameters by considering various circumstances of a process. The circumstances include availability of limited samples for estimating control limits, probability distribution is unknown and collected data are highly skewed. To tackle with the limitations, we have proposed generalized skewness correction

Robust geneenvironment interaction analysis using penalized trimmed regression. J. Stat. Comput. Simul. Pub Date : 20190206
Yaqing Xu,Mengyun Wu,Shuangge Ma,Syed Ejaz AhmedIn biomedical and epidemiological studies, geneenvironment (GE) interactions have been shown to importantly contribute to the etiology and progression of many complex diseases. Most existing approaches for identifying GE interactions are limited by the lack of robustness against outliers/contaminations in response and predictor spaces. In this study, we develop a novel robust GE identification

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates. J. Stat. Comput. Simul. Pub Date : 20180508
M D Koslovsky,M D Swartz,L LeonNovelo,W Chan,A V WilkinsonWe develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectationmaximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment

Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model. J. Stat. Comput. Simul. Pub Date : 20180113
Peter C AustinThe use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets. J. Stat. Comput. Simul. Pub Date : 20180101
Simeone Marino,Nina Zhou,Yi Zhao,Lu Wang,Qiucheng Wu,Ivo D DinovThere are no practical and effective mechanisms to share highdimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical

Feature screening in ultrahighdimensional additive Cox model. J. Stat. Comput. Simul. Pub Date : 20180101
Guangren Yang,Sumin Hou,Luheng Wang,Yanqing SunThe additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahighdimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes

A density based empirical likelihood approach for testing bivariate normality. J. Stat. Comput. Simul. Pub Date : 20180101
Gregory Gurevich,Albert VexlerSample entropy based tests, methods of sieves and Grenander estimation type procedures are known to be very efficient tools for assessing normality of underlying data distributions, in onedimensional nonparametric settings. Recently, it has been shown that the density based empirical likelihood (EL) concept extends and standardizes these methods, presenting a powerful approach for approximating optimal

Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation. J. Stat. Comput. Simul. Pub Date : 20180101
Andrew Holbrook,Shiwei Lan,Alexander VandenbergRodes,Babak ShahbabaWe present geodesic Lagrangian Monte Carlo, an extension of Hamiltonian Monte Carlo for sampling from posterior distributions defined on general Riemannian manifolds. We apply this new algorithm to Bayesian inference on symmetric or Hermitian positive definite matrices. To do so, we exploit the Riemannian structure induced by Cartan's canonical metric. The geodesics that correspond to this metric are

Accurate unconditional pvalues for a twoarm study with binary endpoints. J. Stat. Comput. Simul. Pub Date : 20180101
Guogen Shan,Le Kang,Min Xiao,Hua Zhang,Tao JiangUnconditional exact tests are increasingly used in practice for categorical data to increase the power of a study and to make the data analysis approach being consistent with the study design. In a twoarm study with a binary endpoint, pvalue based on the exact unconditional Barnard test is computed by maximizing the tail probability over a nuisance parameter with a range from 0 to 1. The traditional

Temporal Prediction of Future State Occupation in a Multistate Model from HighDimensional Baseline Covariates via PseudoValue Regression. J. Stat. Comput. Simul. Pub Date : 20171209
Sandipan Dutta,Susmita Datta,Somnath DattaIn many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high throughput genomic and proteomic assays, a clinician may intent to use such high dimensional covariates

Bayesian Computation for LogGaussian Cox Processes: A Comparative Analysis of Methods. J. Stat. Comput. Simul. Pub Date : 20171205
Ming Teng,Farouk S Nathoo,Timothy D JohnsonThe LogGaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doublystochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihoodbased

Fast Bayesian Variable Screenings for Binary Response Regressions with Small Sample Size. J. Stat. Comput. Simul. Pub Date : 20171028
SM Chang,JY Tzeng,RB ChenScreening procedures play an important role in data analysis, especially in highthroughput biological studies where the datasets consist of more covariates than independent subjects. In this article, a Bayesian screening procedure is introduced for the binary response models with logit and probit links. In contrast to many screening rules based on marginal information involving one or a few covariates

Estimation of the linear mixed integrated OrnsteinUhlenbeck model. J. Stat. Comput. Simul. Pub Date : 20170519
Rachael A Hughes,Michael G Kenward,Jonathan A C Sterne,Kate TillingThe linear mixed model with an added integrated OrnsteinUhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood

Joint Models for Multiple Longitudinal Processes and Timetoevent Outcome. J. Stat. Comput. Simul. Pub Date : 20161207
Lili Yang,Menggang Yu,Sujuan GaoJoint models are statistical tools for estimating the association between timetoevent and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a twostage method, Bayesian and maximumlikelihood methods. In this work, we consider joint models of a timetoevent outcome and multiple longitudinal

Concurrent generation of multivariate mixed data with variables of dissimilar types. J. Stat. Comput. Simul. Pub Date : 20161126
Anup Amatya,Hakan DemirtasData sets originating from wide range of research studies are composed of multiple variables that are correlated and of dissimilar types, primarily of count, binary/ordinal and continuous attributes. The present paper builds on the previous works on multivariate data generation and develops a framework for generating multivariate mixed data with a prespecified correlation matrix. The generated data