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Correlationdriven framework based on graph convolutional network for clinical disease classification J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210511
Kai Cao, Ying Xiao, Muzhou HouWith the increasing popularity of computeraided technology applied in medicine, great achievements have been made in certain diseases. However, due to the similarity of clinical and histological features, the problem of disease classification has not been well resolved, especially the Crohn’s disease (CD) and intestinal tuberculosis (ITB). In this paper, a novel sample connection driven framework

Optimal random sample size in progressively TypeII censoring based on cost constraint for the proportional hazards family J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210511
Elham Basiri, Akbar AsgharzadehPerhaps the most frequently asked question concerning sampling is, ‘What sample size do we need?’. The answer to this question is influenced by many factors. Here, we consider the progressively type II censoring and respond to this question by considering the cost of experiment for the proportional hazard rate models. Towards this end, we first introduce a cost function and then by minimizing it we

SLASSO: a scaled LASSO for multicollinear situations J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210511
Mohammad Arashi, Yasin Asar, Bahadır YüzbaşıWe propose a rescaled LASSO by premultiplying the LASSO with a matrix term, namely, scaled LASSO (SLASSO), for multicollinear situations. Our numerical study has shown that the SLASSO is comparable with other sparse modeling techniques and often outperforms the LASSO and elastic net. Our findings open new visions about using the LASSO still for sparse modeling and variable selection. We conclude

ARMA process for speckled data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210507
Pedro M. AlmeidaJunior, Abraão D. C. NascimentoSynthetic aperture radar (SAR) systems are efficient to deal with remote sensing issues. In contrast, SAR images are affected by speckle noise, due to the use of coherent illumination in their capturing. This noise imposes both a granular interference on such images (precluding their interpretability) and a multiplicative and nonGaussian nature on their data. The multiplicative modelling is often

Bayesian inference of a dependent competing risk data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210507
Debashis Samanta, Debasis KunduRecently, Feizjavadian and Hashemi (Analysis of dependent competing risks in presence of progressive hybrid censoring using Marshall–Olkin bivariate Weibull distribution. Comput Stat Data Anal. 2015;82:19–34) provided a classical inference of a competing risks data set using Marshall–Olkin bivariate Weibull distribution when the failure of an unit at a particular time point can happen due to more than

A majorizationminimization scheme for L2 support vector regression J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210428
Songfeng ZhengIn a support vector regression (SVR) model, using the squared ϵinsensitive loss function makes the optimization problem strictly convex and yields a more concise solution. However, the formulation of L2SVR leads to a quadratic programming which is expensive to solve. This paper reformulates the optimization problem of L2SVR by absorbing the constraints in the objective function, which can be solved

Adaptiveweighted estimation of semivarying coefficient models with heteroscedastic errors J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210427
Yuze Yuan, Yong ZhouAn adaptiveweighted estimation procedure for parametric and nonparametric coefficients in semivarying coefficient models with heteroscedastic errors is considered in this paper. Firstly, we present a consistent estimator of the variance function of the error term. In order to take the heteroscedasticity into consideration, we consider the weighted local linear smoothing technique. Asymptotic properties

A Gibbs sampler for learning DAG: a unification for discrete and Gaussian domains J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210425
Hamid Zareifard, Vahid Rezaei Tabar, Dariusz PlewczynskiOne of the major challenges in modern day statistics is to formulate models and develop inferential procedures to understand the complex multivariate relationships present in highdimensional datasets. In this paper, we address the issue of model determination for DAGs, with respect to a given ordering of the variables, together with the corresponding parameter estimation. For this, we use a hierarchical

On the estimation of population size under dependent dualrecord system: an adjusted profilelikelihood approach J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210422
Kiranmoy Chatterjee, Diganta MukherjeeMotivated by various applications in epidemiology, population studies, criminology, etc., the problem of estimating size of a homogeneous human population based on twosample capture–recapture experiment is considered in this article. The Lincoln–Petersen estimate, assuming independence between the samples, is widely used though often its relevance is unanimously criticized. Time and behavioural response

Modified moment estimators based on nonconventional measures for the power function distribution J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210420
Sajjad Haider Bhatti, Muhammad Azeem, Tanvir Ahmad, Muhammad Ali RazaPower function is amougst the most suitable probability models for survival or failure times analysis, particularly of electronic components and product reliability. The article proposes some new modified moment estimators for parameter estimation of the power function distribution. The proposed estimators are based on some nonconventional descriptive measures like harmonic mean, quartile deviation

A fast adaptive Lasso for the cox regression via safe screening rules J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210418
Zhuan Zhang, Zhenyuan Shen, Hong Wang, Shu Kay NgSome interesting recent studies have shown that safe feature elimination screening algorithms are useful alternatives in solving large scale and/or ultrahighdimensional Lassotype problems. However, to the best of our knowledge, the plausibility of adapting the safe feature elimination screening algorithm to survival models is rarely explored. In this study, we first derive the safe feature elimination

Selecting the smoothing parameter and knots for an extension of penalized splines to censored data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210418
Jesus Orbe, Jorge VirtoThe combination of Psplines and Kaplan–Meier weights provide a flexible approach to nonparametric modelling in the context of censored data. To apply this methodology, it is necessary to choose the smoothing parameter and the number and location of the knots. In this paper, we propose a new criterion for choosing the smoothing parameter adapted to the case of uncensored data. In addition, alternatives

Bayesian survival analysis using gamma processes with adaptive time partition J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210410
Yi Li, Sumi Seo, Kyu Ha LeeIn Bayesian semiparametric analyses of timetoevent data, nonparametric process priors are adopted for the baseline hazard function or the cumulative baseline hazard function for a given finite partition of the time axis. However, it would be controversial to suggest a general guideline to construct an optimal time partition. While a great deal of research has been done to relax the assumption of

A Pólya–Gamma sampler for a generalized logistic regression J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210410
Luciana Dalla Valle, Fabrizio Leisen, Luca Rossini, Weixuan ZhuIn this paper, we introduce a novel Bayesian data augmentation approach for estimating the parameters of the generalized logistic regression model. We propose a Pólya–Gamma sampler algorithm that allows us to sample from the exact posterior distribution, rather than relying on approximations. A simulation study illustrates the flexibility and accuracy of the proposed approach to capture heavy and light

KFC: A clusterwise supervised learning procedure based on the aggregation of distances J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210410
Sothea Has, Aurélie Fischer, Mathilde MougeotABSTRACT Nowadays, many machine learning procedures are available on the shelve and may be used easily to calibrate predictive models on supervised data. However, when the input data consists of more than one unknown cluster, linked to different underlying predictive models, fitting a model is a more challenging task. We propose, in this paper, a threestep procedure to automatically solve this problem

Reliability estimation of the stress–strength model with nonidentical jointly typeII censored Weibull component strengths J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210408
Çagatay ÇetinkayaABSTRACT This article considers the estimation of the stress–strength reliability with nonidentical and also jointly censored component strengths. Both stress and strength variables are assumed to follow twoparameter Weibull distribution. In this reliability model, we assume typeII censoring scheme for common stress variable and jointly typeII censoring scheme for strength variables. Inferences

Bayesian inverse regression for supervised dimension reduction with small datasets J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210408
Xin Cai, Guang Lin, Jinglai LiABSTRACT We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors x which can retain the statistical relationship between x and the response variable y. We follow the idea of the sliced inverse regression (SIR) and the sliced average variance estimation (SAVE) type of methods, which is to use the statistical information of the conditional

Coordinate majorization descent algorithm for nonconvex penalized regression J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210408
Yanxin Wang, Li ZhuABSTRACT In this paper, a family of coordinate majorization descent algorithms are proposed for solving the nonconvex penalized learning problems including SCAD and MCP estimation. In the coordinate majorization descent algorithms, each coordinate descent step is replaced with a coordinatewise majorization descent operation, and the convergence of the algorithms are discussed in linear models. In

Comparison of parametric and semiparametric survival regression models with kernel estimation J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210408
Iveta Selingerova, Stanislav Katina, Ivanka HorovaThe modelling of censored survival data is based on different estimations of the conditional hazard function. When survival time follows a known distribution, parametric models are useful. This strong assumption is replaced by a weaker in the case of semiparametric models. For instance, the frequently used model suggested by Cox is based on the proportionality of hazards. These models use nonparametric

The package: nonparametric regression using local rotation matrices in J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210408
Marco Di Marzio, Stefania Fensore, Giovanni Lafratta, Charles C. TaylorABSTRACT The package implements nonparametric (smooth) regression for spherical data in , and is freely available from the Comprehensive Archive Network (CRAN), licensed under the MIT License. It can be used for regression when both the response and explanatory variables lie on the unit sphere. The model uses a flexible kerneltype regression determined by a rotation which depends on a smoothing parameter

Logarithmic calibration for nonparametric multiplicative distortion measurement errors models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210408
Jun Zhang, Xia CuiA logarithmic calibration estimation procedure is proposed for nonparametric regression models under the multiplicative distortion measurement errors setting. The unobservable response variable and covariates are both distorted in a multiplicative fashion by an observed confounding variable. By using the logarithmic calibration estimation procedure for unobserved variables, we consider to study the

Meta fuzzy functions based feedforward neural networks with a single hidden layer for forecasting J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210404
Nihat TakFeedforward neural networks have been frequently used in forecasting problems, recently. In this study, we propose a naive method to improve the forecasting ability of feedforward neural networks with a single hidden layer by adapting meta fuzzy functions. Because neural networks are very sensitive to the initial random weights, usually some numbers of repeats are processed with different initial

Logarithmic minimum test for independence in threeway contingency table of small sizes J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210404
Piotr SulewskiABSTRACT The main aim of this paper is to propose a logarithmic minimum test for the threeway contingency table. The second aim is to compare the quality of independence tests by using their power. Each test has power of test (PoT) function of its own. The PoT functions are determined with the Monte Carlo method and then compared to each other. The third aim is to use a measure named the mean average

Prediction accuracy measures for timetoevent models with lefttruncated and rightcensored data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210401
Feipeng Zhang, Xiaoyan Huang, Caiyun FanABSTRACT Many timetoevent models have been developed for lefttruncated and rightcensored (LTRC) data, which arise in many applications involving followup studies. However, there is no work on evaluating the prediction accuracy of the timetoevent models for LTRC data. This paper develops two novel weighted prediction summary measures for a nonlinear prediction function with LTRC data. They are

Maximum likelihood and maximum a posteriori estimators for the Riesz probability distribution J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210401
Sameh Kessentini, Raoudha ZineWe focus on some statistical facets of the Riesz probability distribution that could replace and exceed the Wishart in many application fields. First, the maximum likelihood (ML) estimators of both Riesz parameters are derived using two approaches. The first one yields an equation solved using an algorithm alternating the Cholesky decomposition with intermediate calculations. The second one provides

Fuzzy cmeans clustering with conditional probability based K–L information regularization J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210401
Ouafa Amira, JiangShe Zhang, Junmin LiuFuzzy cmeans with regularization by K–L information (KLFCM) is an objective function method for clustering, which is regarded as a fuzzy counterpart of Gaussian mixture models (GMMs) with EM algorithm when the regularization parameter λ equals 2. However, KLFCM method extracts very close or even coincident clusters in many cases because the K–L information term in its objective function is used to

Modeling and inference for counts time series based on zeroinflated exponential family INGARCH models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210401
Sangyeol Lee, Dongwon Kim, Seongwoo SeokThis study considers statistical inferences such as parameter estimation and change tests for counts time series models where the conditional density of present observations over past information follow a zeroinflated oneparameter exponential family. We verify that the zeroinflated exponential family (ZIEF) INGARCH process is stationary and ergoic, and the maximum likelihood estimator is consistent

Adaptive multivariate EWMA charts for monitoring sparse mean shifts based on parameter optimization design J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210330
Wei Zhao, Zhijun Wang, Chunjie WuHighdimensional data is becoming increasingly important in modern manufacturing environments, while brings difficulties for process monitoring at the same time, especially for the detection of sparse mean shifts. Several control schemes like VSMEWMA and LEWMA have been proposed on basis of variable selection techniques recently. However, these schemes with constant smoothing parameters may perform

An integrated quality, maintenance and production model based on the delayed monitoring under the ARMA control chart J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210330
Samrad JafarianNamin, Mohammad Saber Fallahnezhad, Reza TavakkoliMoghaddam, Ali Salmasnia, Seyyed Mohammad Taghi Fatemi GhomiFew studies have investigated the integration of triple components including statistical process monitoring (SPM), maintenance policy (MP), and economic production quantity (EPQ). Of those, no research is found to monitor autocorrelated data. Moreover, although delayed monitoring (DM) policy was proposed for an integrated model of SPM and MP, it has not been extended by considering EPQ. We propose

On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210330
G. I. Papayiannis, G. N. Domazakis, D. Drivaliaris, S. Koukoulas, A. E. Tsekrekos, A. N. YannacopoulosClustering schemes for uncertain and structured data are considered relying on the notion of Wasserstein barycenters, accompanied by appropriate clustering indices based on the intrinsic geometry of the Wasserstein space. Such type of clustering approaches are highly appreciated in many fields where the observational/experimental error is significant or the data nature is more complex and the traditional

Bayesian variable selection in clustering highdimensional data via a mixture of finite mixtures J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210330
Woojin Doo, Heeyoung KimWhen clustering highdimensional data, it is often important to identify variables that discriminate the clusters. Meanwhile, a common issue in clustering is to determine the number of clusters. In this study, we propose a new method that simultaneously performs clustering and variable selection, while inferring the number of clusters from the data. We formulate the clustering problem using a finite

Inference for a general family of inverted exponentiated distributions with partially observed competing risks under generalized progressive hybrid censoring J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210330
Chandrakant Lodhi, Yogesh Mani Tripathi, Liang WangIn this paper, statistical inference for a competing risks model is discussed when latent failure times belong to a general family of inverted exponentiated distributions. Based on a generalized progressive hybrid censored data with partially observed failure causes, estimations for unknown parameters are presented under nonrestricted and restricted parameter cases from classic and Bayesian perspectives

A comparison of estimation methods for reliability function of inverse generalized Weibull distribution under new loss function J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210323
A. Amirzadi, E. Baloui Jamkhaneh, E. DeiriIn this paper, we focussed on the scale parameter and reliability estimations of the inverse generalized Weibull distribution. Both classical and Bayesian approaches are considered with various loss functions as general entropy, squared log error and weight squared error. For the Bayesian method, both informative and noninformative priors are applied for the reliability and scale parameter estimation

Inference based on partly interval censored data from a twoparameter Rayleigh distribution J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210319
Riyadh Rustam AlMosawi, Sanku DeyIn this paper, the maximum likelihood and Bayesian estimation of the parameters of locationscale Rayleigh distribution with partly interval censored data is considered. For computing the maximum likelihood estimators with partly interval censored data, three methods are used, namely, NewtonRaphson, ExpectationMaximization and MonteCarlo ExpectationMaximization algorithms. The standard errors of

Approximately optimal subset selection for statistical design and modelling J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210319
Yu Wang, Nhu D. Le, James V. ZidekWe study the problem of optimal subset selection from a set of correlated random variables. In particular, we consider the associated combinatorial optimization problem of maximizing the determinant of a symmetric positive definite matrix that characterizes the chosen subset. This problem arises in many domains, such as experimental designs, regression modelling, and environmental statistics. However

Extraction and organization of statistical distribution functions for simulation of variations and patterns in the variability control charts J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210319
S. A. Lesany, S. M. T. Fatemi GhomiAlthough the existence of natural variations in process control charts is inevitable, the formations of significant patterns associate outofcontrol conditions. Hence, the detection of unnatural patterns is essential to increase the sensitivity of Shewhart’s control charts. In the previous years, numerous models have been offered for recognizing and analysing nonrandom patterns. These models commonly

Cryptocurrency direction forecasting using deep learning algorithms J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210316
Mahdiye Rahmani Cherati, Abdorrahman Haeri, Seyed Farid GhannadpourRecently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price direction of the BTC/USD. Both model accuracy and the profit or loss of the trades made based on the proposed model are analyzed. In addition, the effects of the MACD indicator and the input matrix dimension on

Improved point estimation for inverse gamma regression models J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210312
Tiago M. Magalhães, Diego I. Gallardo, Marcelo BourguignonThis paper develops a bias correction scheme for reparametrized inverse gamma regression models with varying precision [Bourguignon M, Gallardo DI. Reparametrized inverse gamma regression models with varying precision. Stat Neerl. 2020;74(4):611–627], which is tailored to situations where the response variable has an asymmetrical shape on the positive real line. In particular, we discuss maximumlikelihood

Nonparametric estimation of singleindex models in scalespace J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210312
Jib Huh, Derek Dyal, Cheolwoo ParkMultivariate nonparametric regression faces great challenges when overburdened with large amounts of covariates. For this reason, singleindex models (SIMs) have been frequently used for reducing the parameters to be estimated in nonparametric and semiparametric models. In this paper, we develop a scalespace statistical tool, known as significant zero crossings of derivatives (SiZer), for SIM. It

Defining a twoparameter estimator: a mathematical programming evidence J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210312
Gülesen Üstündağ Şiray, Selma Toker, Nimet ÖzbayTwoparameter (TP) estimators are more advantageous to their oneparameter competitors since they have two biasing parameters that serve different purposes in linear regression model. At least one of these biasing parameters intends to gain a remedial impact for multicollinearity. Within this respect, we define a new TP estimator to eliminate the disorder originated from multicollinearity. Also, we

Novel model selection criteria for LMARS: MARS designed for biological networks J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210311
Gül Bahar Bülbül, Vilda PurutçuogluABSTRACT In higher dimensions, the loopbased multivariate adaptive regression splines (LMARS) model is used to build sparse and complex gene structure nonparametrically by correctly defining its interactions in the network. Also, it prefers to apply the generalized crossvalidation (GCV) value as its original model selection criterion in order to select the best model, in turn, represent the true

Global onesample tests for highdimensional covariance matrices J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210309
Xiaoyi Wang, Baisen Liu, NingZhong Shi, GuoLiang Tian, Shurong ZhengTesting highdimensional covariance matrix plays an important role in multivariate statistical analysis. Many statisticians used the statistics based on tr[(ΣΣ0−1−Ip)2] to test H0:Σ=Σ0 with Σ being the covariance matrix. However, none have proposed a statistic based on tr[(Σ−Σ0)2] for this purpose. In fact, neither of the two tests is superior to the other based on their powers because they target

A zeromodified Poisson mixed model with generalized random effect J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210307
Gabriela C. Raquel, Katiane S. Conceição, Marcos O. Prates, Marinho G. AndradeIn this paper, we present an extension of the Poisson ZeroModified model with Normal and Generalized LogGamma random effects. The random effect induces correlation and accommodate the intrinsic variability of each individual. The Generalized LogGamma effect is a generalized Normal effect and can be used in atypical situations where the Normal effect is not appropriate. In particular, the mixed ZeroModified

Generalized fiducial inference for the Lomax distribution J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210307
Liang Yan, Juan Geng, Lijun Wang, Daojiang HeFor the point and interval estimation of the scale and shape parameters of the Lomax distribution, the frequentist method is invalid and the Bayesian method is sometimes inefficient when the coefficient of variation is less than one. In particular, when the coefficient of variation and the sample size are small, the phenomenon is getting worse that one needs to develop an effective and robust approach

Performance of adaptive exponentially weighted moving average control chart in the presence of measurement error J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210307
Muhammad NoorulAmin, Afshan RiazA control chart is one of the important industrial tools used for monitoring the stability of manufacturing processes. The performance of the control charts can be affected in the presence of measurement error, which also leads to erroneous conclusions. In this article, we examined the effect of measurement error on an adaptive exponentially weighted moving average (AEWMA) control chart by using simple

Enhanced adaptive multivariate EWMA and CUSUM charts for process mean J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210304
Abdul Haq, Michael B. C. Khoo, Ming Ha Lee, Saddam Akber AbbasiThe multivariate charts are mostly used to simultaneously monitor several quality characteristics in manufacturing processes. In this study, we enhance the sensitivities of the recently proposed adaptive multivariate EWMA (AME) and weighted adaptive multivariate CUSUM (WAMC) charts with an auxiliaryinformationbased (AIB) estimator, namely the AIBAME and AIBWAMC charts, for monitoring different

Redrawingresampling rejection controlled sequential importance sampling J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210304
Xuhua Liu, Na LiMonte Carlo computation has been widely applied in the field of dynamic systems. This paper focuses on the general framework in the implementation of sequential importance sampling by combining redrawing, resampling and rejection control simultaneously. The proposed algorithm is named as Redrawing Resampling Rejection Controlled Sequential Importance Sampling (RRRCSIS). It can reduce sampling computation

Particle swarm stepwise (PaSS) algorithm for information criteriabased variable selections J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210304
RayBing Chen, ChienChih Huang, Weichung WangA new stochastic search algorithm is proposed for solving informationcriterionbased variable selection problems. The idea behind the proposed algorithm is to search for the best model for the previously specified information criterion using multiple search particles. These particles simultaneously explore the candidate model space and communicate with each other to share search information. A new

Quantifying the ratioplot for the geometric distribution J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210301
Bojana Milošević, M. Dolores JiménezGamero, M. Virtudes AlbaFernándezThe 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 distribution in terms of a differential equation involving its probability generating function. Several ways of looking at the characterization allow us to derive six test statistics. The connection between some

Truncated normal distributionbased EWMA control chart for monitoring the process mean in the presence of outliers J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210226
Wushuang Tan, Liu LiuIn many applications, it is very important to detect outliers during the analysis of normal data. Many existing methods preprocess the data to remove the outliers and then analyse the data accordingly when the data are contaminated by different unexpected outliers; however, it is difficult to use this method for applications where the data must be analysed online. In this article, we present an online

Model averaging for multiple quantile regression with covariates missing at random J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210226
Xianwen Ding, Jinhan Xie, Xiaodong YanABSTRACT In this paper, we develop a model averaging estimation procedure for multiple quantile regression where missingness occurs to the covariates. Our concern is on the improvement of prediction accuracy for multiple quantiles of response conditional on observed covariates. A set of candidate models is constructed according to missingness data patterns. In this model set, one model is based on

Sensitivity analysis and visualization for functional data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210226
IChung Hsieh, Yufen HuangABSTRACT When analyzing functional data processes, the presence of outliers can greatly influence modelling and forecasting outcomes and lead to the inaccurate conclusion. Hence, detection of such outliers becomes an essential task. Visualization of data not only plays a vital role in discovering the features of data before applying statistical models and summary statistics but also provides an auxiliary

Nonparametric estimation of the extropy and the entropy measures based on progressive typeII censored data with testing uniformity J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210222
Raja Hazeb, M. Z. Raqab, Husam Awni BayoudABSTRACT The extropy measure is a complementary dual function of Shannon entropy which was proposed by Lad et al. Extropy: complementary dual of entropy. Stat Sci. 2015;30:40–58. This measure of uncertainty have received a considerable attention in the last five years. In this paper, several methods of estimation for the extropy and entropy measures based a progressively TypeII censored data are derived

Optimal economic statistical design of combined double sampling and variable sampling interval multivariate T 2 control charts J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210216
Mehdi Katebi, Michael B. C. KhooABSTRACT Recent studies have shown that an adaptive T 2 chart with double sampling and variable sampling interval D S V S I T 2 chart shows a good performance in detecting small to moderate shifts in the mean vector. This paper develops an economic statistical model for the D S V S I T 2 chart. The statistical performance of the proposed establish is evaluated using the average number of false alarms

Classical and Bayesian estimation of the index C pmk and its confidence intervals for normally distributed quality characteristic J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210216
Sanku Dey, Chunfang Zhang, Mahendra SahaIn this article we consider the process capability index (PCI) $C_{pmk}$ which can be used for normal random variables. The objective of this article is four fold: first we address the different classical methods of estimation of the PCI $C_{pmk}$ from frequentest approaches for the normal distribution and compare them in terms of their biases and mean squared errors. Second, we compare three bootstrap

Double bootstrapping for visualizing the distribution of descriptive statistics of functional data J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210210
Han Lin ShangWe propose a double bootstrap procedure for reducing coverage error in the confidence intervals of descriptive statistics for independent and identically distributed functional data. Through a series of Monte Carlo simulations, we compare the finite sample performance of single and double bootstrap procedures for estimating the distribution of descriptive statistics for independent and identically

A nearestneighborbased ensemble classifier and its largesample optimality J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210210
Majid Mojirsheibani, William PouliotABSTRACT A nonparametric approach is proposed to combine several individual classifiers in order to construct an asymptotically more accurate classification rule in the sense that its misclassification error rate is, asymptotically, at least as low as that of the best individual classifier. The proposed method uses a nearest neighbour type approach to estimate the conditional expectation of the class

Are most proposed ridge parameter estimators skewed and do they have any effect on MSE values? J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210208
Selman Mermi, Atila Göktaş, Özge AkkuşMulticollinearity is a common problem in multiple regression that occurs whenever two or more explanatory variables are highly correlated. When multicollinearity exists, the method of Ordinary Least Square (OLS) is likely to produce poor parameter estimates. Furthermore, OLS estimates of regression coefficients can be inflated making them too large. Ridge regression is one of the most popular methods

TFMIDAS: a transfer function based mixedfrequency model J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210207
Nicolás BoninoGayoso, Alfredo GarciaHiernauxABSTRACT This paper tackles the mixedfrequency modelling problem from a new perspective. Instead of drawing upon the common distributed lag polynomial model, we use a transfer function representation to develop a new type of models, named TFMIDAS. We derive the theoretical TFMIDAS implied by the highfrequency VARMA family models for two common aggregation schemes, flow and stock. This exact correspondence

Semiparametric analysis of case K intervalcensored failure time data in the presence of a cured subgroup and informative censoring J. Stat. Comput. Simul. (IF 0.918) Pub Date : 20210204
Shuying Wang, Da Xu, Chunjie Wang, Jianguo SunInterval censoring and the existence of a cured subgroup occur quite often in survival studies and many procedures have been developed for dealing with each of the two issues individually or them together. In this paper, we discuss the situation where both issues exist and furthermore, interval censoring may be informative, for which there exists relatively much less literature. For the problem, we