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Performance of ridge estimator in skewnormal mode regression model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210125
Xinyun Cao; Danlu Wang; Liucang WuAbstract A large amount of literature has been developed for the presence of multicollinearity among the explanatory variables that are often performed with the aim of reducing the undesirable effects on the maximum likelihood estimate(MLE). In particular, many authors have discussed ridge estimation (RE) under the framework of the mean regression model, because the RE enjoys the advantage that its

A firstorder integervalued autoregressive process with zeromodified PoissonLindley distributed innovations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210125
M. Sharafi; Z. Sajjadnia; A. ZamaniAbstract In this paper, we introduce a firstorder integervalued autoregressive process with zeromodified PoissonLindley distributed innovations based on the binomial thinning operator. Some statistical and conditional properties of the model are presented, several estimation methods are applied to estimate the unknown parameters and, for the YuleWalker and conditional least square estimators,

New modelaveraged estimators of concordance correlation coefficients: simulation and application to longitudinal overdispersed Poisson data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210122
MiaoYu Tsai; ChaoChun LinAbstract The concordance correlation coefficient (CCC) is a common tool to assess agreement among multiple observers for continuous and discrete responses. However, previous results in the statistical literature have shown that the CCC estimators may suffer from a bias problem under a misspecified model for normal data. In order to avoid fitting data with a misspecified model, thus yielding biased

Monitoring exponentially distributed time between events data: selfstarting perspective Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210121
Eralp Dogu; Muhammad NoorulAminAbstract Time between events (TBE) control charts have been widely used to monitor high yield processes. Traditionally, an estimated incontrol occurrence rate from a Phase I dataset is used to calculate the control limits when the rate is unknown. However, when Phase I analysis is time consuming or costly, the traditional Phase I/Phase II approach is not feasible. A selfstarting method that sequentially

Modified onesided EWMA charts for monitoring time between events Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210121
XueLong Hu; YuLong Qiao; PanPan Zhou; JianLan Zhong; Shu WuAbstract Control charts based on Time Between Events (TBE) have been shown to be effective in high quality processes, where the defects or failures (events) occur at a very low probability. The occurrences of events are usually modeled as a homogeneous Poisson process. The TBE data is likely to follow a skewed distribution, such as the gamma distribution. In order to monitor high quality processes

Forecasting stock prices using hybrid nonstationary time series model with ERNN Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210121
Dileep Kumar Shetty; B. IsmailAbstract In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed a hybrid nonstationary model with Elman’s Recurrent Neural Networks (ERNN). The proposed model is nonstationary in trend component with lagged variable, average of all past observations and ERNN. This model can capture both linear and nonlinear

Conditional least squares estimation for the SINAR(1, 1) process Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210121
Gilberto Pereira Sassi; Carolina Costa Mota ParaíbaAbstract This paper presents the conditional least squares (CLS) estimation procedure for the class of firstorder spatial integervalued autoregressive processes, denote by SINAR(1, 1). We derive the asymptotic properties of the CLS estimators of model parameters. Simulation results are presented to assess the performance of estimators under finite sample sizes and under equidispersion and overdispersion

Price bubbles in Beijing carbon market and environmental policy announcement Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210120
Min Lu; Xing Wang; Rosalie SpeeckaertAbstract This paper examines price bubbles in the relatively new carbon emission trading scheme of Beijing carbon market by employing a recently proposed econometric test which can stamp the occurrence and burst of financial bubbles. We find multiple bubbles in Beijing carbon market over the sample period between January 2014 to April 2018, and that the occurrences of carbon price bubbles are closely

Complete convergence for randomly weighted sums of random variables and its application in lineartimeinvariant systems Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210119
Junjun Lang; Tianyun He; Zhiqiang Yu; Yi Wu; Xuejun WangAbstract In this paper, some results on the complete convergence for randomly weighted sums of widely orthantdependent (WOD, for short) random variables are established under some general conditions, extending the corresponding ones obtained in the literature. By using the results that we presented, we further investigate the convergence of the state observers of lineartimeinvariant systems based

A comparison of methods for clustering longitudinal data with slowly changing trends Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210119
N. G. P. Den Teuling; S. C. Pauws; E. R. van den HeuvelAbstract Longitudinal clustering provides a detailed yet comprehensible description of time profiles among subjects. With several approaches that are commonly used for this purpose, it remains unclear under which conditions a method is preferred over another method. We investigated the performance of five methods using Monte Carlo simulations on synthetic datasets, representing various scenarios involving

Employing long shortterm memory and Facebook prophet model in air temperature forecasting Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210119
Toni Toharudin; Resa Septiani Pontoh; Rezzy Eko Caraka; Solichatus Zahroh; Youngjo Lee; Rung Ching ChenAbstract One of information needed in weather forecast is air temperature. This value might change any time. Prediction of air temperature is very valuable for some communities and occasions. Therefore, high accuracy prediction is needed. Since the information about air temperature might vary over time, it is necessary to implement methods that can adapt to this situation. The use of neural network

A caution in the use of multiple criteria for selecting working correlation structure in generalized estimating equations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210117
Abida Sultana; Nasrin Lipi; Ajmery JamanAbstract The generalized estimating equations (GEE) procedure is a common approach for regression analyses of correlated responses, which is common for data obtained in longitudinal studies. Selecting a working correlation structure has been a momentous topic in longitudinal data analysis using GEE. Although consistent and asymptotically normal estimates can be obtained with misspecified working correlation

Comparison of generalized estimating equations and QuasiLeast Squares regression methods in terms of efficiency with a simulation study Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210117
Erdoğan Asar; Erdem KarabulutAbstract Generalized Estimating Equations (GEE) is used to analyze repeated measurements taken from subjects at equal time intervals and is applicable in presence of missing data. In this study, we aimed to introduce QuasiLeast Squares Regression (QLS), which is an extension of GEE and applicable when time intervals are unequal, and compare model performances under different scenarios in terms of

On approximations of value at risk and expected shortfall involving kurtosis Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210117
Mátyás Barczy; Ádám Dudás; József GállAbstract We derive new approximations for the Value at Risk and the Expected Shortfall at high levels of loss distributions with positive skewness and excess kurtosis, and we describe their precisions for notable ones such as for exponential, Pareto type I, lognormal and compound (Poisson) distributions. Our approximations are motivated by that kind of extensions of the socalled Normal Power Approximation

Comparison of correlation measures for nominal data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210115
Tanweer Ul Islam; Mahvish RizwanAbstract In social sciences, a plethora of studies utilize nominal data to establish the relationship between the variables. This, in turn, requires the correct use of correlation technique. The choice of correlation technique depends upon the underlying assumptions and power of the test of significance. The objective of the research is to explore the best measure of association for nominal data in

Parameter estimation for diffusion process from perturbed discrete observations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210112
Dang Duc Trong; Thai Phuc HungAbstract We study the parameter estimation for ergodic diffusion process Xt from perturbed observations Y t j = X t j + ε t j , j = 1 , … , n where t 1 , … , t n are observation times and the noise ε t is a strongly mixing stationary noisy process with the density function g. We construct an estimator of the diffusion parameters based on the minimum Hellinger distance between the density of the invariant

An alternative test for zero modification in the INAR(1) model with Poisson innovations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210112
Jie Huang; Fukang ZhuAbstract Several methods have been proposed for detecting zero modification in the firstorder integervalued autoregressive (INAR(1)) process. A basic problem of these tests is that they rely upon asymptotic results. In this paper, an alternative test is introduced which makes direct use of the approximate distribution of the number of zeros, which can be described by a betabinomial distribution

Median bias reduction in cumulative link models Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210108
V. Gioia; E. C. Kenne Pagui; A. SalvanAbstract This paper presents a novel estimation approach for cumulative link models, based on median bias reduction. The median bias reduced estimator is obtained as solution of an estimating equation based on an adjustment of the score. It allows to obtain higherorder median centering of maximum likelihood estimates without requiring their finiteness. The estimator is equivariant under componentwise

Testing for central symmetry and symmetry about an axis Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210103
S. Riahi; P. N. PatilAbstract Out of the many different types of symmetries of a continuous bivariate joint probability density function, focus here is on the central symmetry and symmetry about an axis. Tests for both types of symmetries are proposed and evaluated in the literature. However interestingly, investigation into tests for and a general discussion of either central symmetry or symmetry about an axis don’t seem

Quantile regression for compositional covariates Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210103
Xuejun Ma; Ping ZhangAbstract Quantile regression is a very important tool to explore the relationship between the response variable and its covariates. Motivated by mean regression with LASSO for compositional covariates proposed by Lin et al. (Biometrika 101 (4):785–97, 2014), we consider quantile regression with nopenalty and penalty function. We develop the computational algorithms based on linear programming. Numerical

Bayesian networks: regenerative Gibbs samplings Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210101
Do Le Paul MinhAbstract Gibbs samplings is a Markov Chain Monte Carlo technique for estimating conditional probabilities in Bayesian networks. A major problem of Gibbs sampling is the dependency of the generated chain of samples. Thus the estimates are biased unless the initial value of the chain is drawn from the target distribution. One elegant method to overcome the initial bias is regenerative samplings. We reported

Score predictor factor analysis as a tool for the identification of singleitem indicators Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201229
André Beauducel; Norbert HilgerAbstract Score Predictor Factor Analysis (SPFA) was introduced as a method to compute factor score predictors that are – under some conditions – more highly correlated with the common factors resulting from factor analysis than the factor score predictors computed from the factor model. In the present study, we investigate SPFA as a model in its own rights. In order to provide a basis for this, the

Svplots for identifying characteristics of the distribution and testing hypotheses Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201225
Uditha Amarananda WijesuriyaAbstract The histogram and boxplot are effective and simple graphical tools, which are broadly used to explore the characteristics of the distribution of univariate data. In this proposed work, two statistical plots called sample variance plots (Svplots), are defined which illustrate squared deviations from the sample variance. Svplots exhibit the contribution of each data value toward the sample

Pricing vulnerable options under jump diffusion processes using double Mellin transform Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201224
Cuixiang Li; Huili Liu; Lixia Liu; Qiumei YaoAbstract In this paper, we price the vulnerable option under the assumption that both underlying and counterparty asset price follow correlated jump diffusion processes. By the twodimensional ItôDoeblin formula, we initially derive a partial integrodifferential equation (PIDE) satisfied by the price of the vulnerable option with flexible jumps. Then, the double Mellin transform converts the PIDE

Bayesian sampling plan for the exponential distribution with generalized Type  II hybrid censoring scheme Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201224
Deepak Prajapati; Sharmishtha Mitra; Debasis KunduAbstract In this paper, a decisiontheoretic approach is used to obtain the Bayesian sampling plan (BSP) for the generalized TypeII hybrid censoring scheme when lifetimes of sampled units follow a oneparameter exponential distribution. An efficient loss function is used to decide whether to accept or reject the batch. The BSP is obtained by constructing the closed form of the Bayes decision function

General construction of minimal balanced repeated measurements designs in noncircular periods of k different sizes Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201224
Muhammad Abdullah; H. M. Kashif Rasheed; Rashid AhmedAbstract Repeated measurements designs (RMDs) are most important and useful designs in research of various fields due to their economical property but residual or carry over effects are integral parts of these designs. To cope with these problems, balanced or strongly balanced RMDs should be used. In this manuscript Generators are developed to obtain balanced, strongly balanced and weakly balanced

An effective approach towards efficient estimation of general linear model in case of heteroscedastic errors Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201224
Sajjad Haider Bhatti; Faizan Wajid Khan; Muhammad Irfan; Muhammad Ali RazaAbstract Aiming at minimizing the ratio of error with respect to the response variable, the least squares ratio is a relatively new method for estimating the regression parameters. In the current article, the performance of this new approach is compared with the traditional OLS approach in the case when homoscedasticity of errors assumption is violated. A comparison is made through a simulation study

Twosample multivariate tests for highdimensional data when one covariance matrix is unknown Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201223
Nittaya Thonghnunui; Samruam Chongcharoen; Knavoot JiamwattanapongAbstract In this study, the test statistics for onesided and twosided multivariate hypotheses to the highdimensional twosample problem with one unknown covariance matrix were proposed. The tests were developed based on the idea of keeping as much information as possible from the pooled sample covariance matrix by arranging the blocks along its diagonal. The asymptotic distributions of the test

On the weighted tests of independence based on Bernstein empirical copula Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201222
Burcu Hudaverdi; Selim Orhun SusamAbstract In this article, we propose a flexible weighted test of independence based on Cramérvon Mises statistic of the Bernstein empirical copula process. We investigate the asymptotic properties of the new weighted test. A Monte Carlo study is conducted to measure the power performance of the new test under various dependence structures and also to compare the performance with some other alternative

MCMC interweaving strategy for estimating stochastic volatility model and its application Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201222
Chengen Li; Jianhua ShiAbstract Efficient estimation for a stochastic volatility (SV) model has been actively pursued in recent years. In this paper, a new Markov chain Monte Carlo (MCMC) algorithm based on a combination of Kalman filtering and the auxiliary sufficiency interweaving strategy (ASIS) is studied. Compared to other MCMC strategies like Stan algorithm (“Rstan”) and the Gibbs algorithm (“R2Winbugs”), it is shown

Upper record values from the generalized Pareto distribution and associated statistical inference Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201222
Xu Zhao; Shaojie Wei; Weihu Cheng; Pengyue Zhang; Yang Zhang; Qi XuAbstract We investigate point estimation and confidence interval estimation for the heavytailed generalized Pareto distribution (GPD) based on the upper record values. When the shape parameter is known, the biascorrected moments estimators and maximum likelihood estimators (MLE) for the location and scale parameters are derived. However, in practice, the shape parameter is typically unknown. We propose

Bayesian estimator of multiple Poisson means assuming two different priors Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201222
Toru Ogura; Takemi YanagimotoAbstract This paper describes an empirical Bayesian estimator of multiple Poisson means based on a novel concept. The idea is to assume two different priors; Jeffreys’ prior for determining the hyperparameter and the uniform prior for the canonical parameter for estimating multiple Poisson means. The validity of this idea is discussed in detail. Furthermore, the empirical Bayesian estimator is constructed

Moving average and autoregressive correlation structures under multivariate skew normality Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201221
Timothy Opheim; Anuradha RoyAbstract This article explores the parameter space of multivariate skew normal families having identical distributed marginal distributions under a few autoregressivemoving average correlation structures ( Ω ¯ ): namely, MA(1), MA(2), and AR(2) correlation structures. Such an undertaking escapes triviality since the efficacious { Ω ¯ , δ } parametrization, in terms of analysis of marginal distributions

Rescaled bootstrap confidence intervals for the population variance in the presence of outliers or spikes in the distribution of a variable of interest Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201221
P. J. Moya; Juan F. Munoz; Encarnación Álvarez Verdejo; F. J. BlancoEncomiendaAbstract Confidence intervals for the population variance in the presence of outliers or spikes in the distribution of a variable of interest are topics that have not been investigated in depth previously. Results derived from a first Monte Carlo simulation study reveal the limitations of the customary confidence interval for the population variance when the underlying assumptions are violated, and

A general class of estimators in stratified random sampling Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201221
Kuldeep Kumar Tiwari; Sandeep Bhougal; Sunil KumarAbstract In the present paper, a general class of estimators has been proposed for estimating the population mean of the study variable using auxiliary variable when the population mean of the auxiliary variable is known in stratified random sampling. The bias and mean squared error of the proposed class of estimators are derived under stratified sampling to the first degree of approximation. Members

Empirical likelihood ratio tests for two sample comparison under current status data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201217
JinJian Hsieh; TaiYu Hsu; KeYu HouAbstract This article focuses on the two sample test of the survival function of the failure time under the current status data and introduces three types of the nonparametric two sample test of the survival function. This paper constructs the empirical likelihood ratio test with a weighted approach for the two sample comparison based on MLE, SMLE, and MSLE. A bootstrap method is given for constructing

Statistical inference for doubly geometric process with Weibull interarrival times Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201216
Mustafa Hilmi Pekalp; Gültaç Eroğlu İnan; Halil AydoğduAbstract In recent years, the doubly geometric process is started to use as a model to fit the data from a series of events since it provides a more flexible model for wider application than the geometric process. In the applications of the doubly geometric process, the estimation problem associated with the process can arise. In this study, we deal with this problem for the stochastic process model

Absolute logarithmic calibration for correlation coefficient with multiplicative distortion Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201215
Jun Zhang; Zhuoer Xu; Zhenghong WeiAbstract This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a multiplicative fashion by an observed confounding variable. We propose a new identifiability condition by using the absolute logarithmic calibration to obtain calibrated variables and the directplugin estimator for the correlation coefficient

A generalized class of exponential type estimators for estimating the mean of the sensitive variable when using optional randomized response model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201212
Zara Waseem; Hina Khan; Javid Shabbir; Shane FatimaAbstract In this article, we propose a generalized class of exponential type estimators for estimating the mean of delicate variable in the existence of nonsensitive two auxiliary variables. The performance of mean estimator is obtained by using generalized two stage optional randomized response model. The expression for the bias and the mean square error are obtained up to first degree of approximation

A general class of calibration estimators under stratified random sampling in presence of various kinds of nonsampling errors Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201209
G. N. Singh; D. Bhattacharyya; A. BandyopadhyayAbstract This paper addresses the issue of estimating the population variance of a study character in the joint presence of random nonresponse and measurement errors and its application for estimating variations in biological data. Additional information on two highly positively correlated auxiliary variables has been incorporated to develop a general class of estimators under stratified twophase

Bayesian analysis of misclassified binomial data: doublesampling and the zeronumerator problem Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201209
Noriah M. AlKandari; Paul H. GarthwaiteAbstract This article examines the zero numerator problem. This problem occurs when the misclassification rates are so low that both forms of misclassification may not both be present in the validation data. For this problem, the article compares two Bayesian methods for analyzing misclassified binary data that has a validation substudy and three forms of a noninformative prior distribution. It shows

Estimation and testing of multivariate random coefficient autoregressive model based on empirical likelihood Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201209
Jin Chen; Dehui Wang; Cong Li; Jingwen HuangAbstract This paper studies the empirical likelihood method for a multivariate firstorder random coefficient autoregressive (RCAR(1)) model. Based on the modified least square score equation, empirical likelihood method is used to test the randomness of coefficients and estimate the unknown parameters. First, the limiting distribution of the log empirical likelihood ratio statistic is established

Laws of large numbers and complete convergence for WOD random variables and their applications Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201208
Chi Yao; Yongping He; Rui Wang; Xuejun WangAbstract By using the MarcinkiewiczZygmund type inequality and Rosenthal type inequality, we study the Lr convergence, weak law of large numbers and the complete convergence for usual normed sums and weighted sums of arrays of rowwise widely orthant dependent (WOD, for short) random variables. In addition, some applications of the Lr convergence and the complete convergence to nonparametric regression

Application of iterative hybrid MI approach to household survey data with complex dependence structures Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201208
Humera Razzak; Christian HeumannAbstract The multiple indicator cluster survey (MICS) is a household survey tool designed to obtain internationally comparable, statistically rigorous data of standardized indicators related to the health situation of children and women. Missing data in a large number of categorical variables are a serious concern for MICS, following complex dependency structures and inconsistency problems that impose

An evaluation of a novel approach for clustering genes with dissimilar replicates Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201208
Ozan Cinar; Cem Iyigun; Ozlem IlkAbstract Clustering the genes is a step in microarray studies which demands several considerations. First, the expression levels can be collected as timeseries which should be accounted for appropriately. Furthermore, genes may behave differently in different biological replicates due to their genetic backgrounds. Highlighting such genes may deepen the study; however, it introduces further complexities

Dynamic lifetime prediction using a Weibullbased bivariate failure time model: a metaanalysis of individualpatient data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201208
Sayaka Shinohara; YuanHsin Lin; Hirofumi Michimae; Takeshi EmuraAbstract Predicting timetodeath for patients is one of the most important issues in survival analysis. A dynamic prediction method using a bivariate failure time model allows one to build a prediction formula based on tumor progression status observed during the followup. However, the existing spline models for the baseline hazard functions are not convenient for predicting longterm survival probability

Handling missingness value on jointly measured timecourse and timetoevent data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201208
Gajendra K. Vishwakarma; Atanu Bhattacharjee; Souvik BanerjeeAbstract Joint modeling technique is a recent advancement in effectively analyzing the longitudinal history of patients with the occurrence of an event of interest attached to it. This procedure is successfully implemented in biomarker studies to examine parents with the occurrence of tumor. One of the typical problem that influences the necessary inference is the presence of missing values in the

Sterling interpolation method for precision estimation of total least squares Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201207
Leyang Wang; Yingwen Zhao; Chuanyi Zou; Fengbin YuAbstract The randomness of the biases of parameter estimates, residuals and some variables is seldom considered for calculating covariance matrix of parameter estimates in total least squares (TLS) iterative algorithm. There are few studies on the precision estimation with the approximate probability distribution method of function in the TLS. In order to get more reasonable precision information for

The generalized new twotype parameter estimator in linear regression model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201202
Amir Zeinal; Mohammad Reza Azmoun Zavie KiviAbstract In this paper, a new twotype parameter estimator is proposed. This estimator is a generalization of the new two parameter (NTP) estimator introduced by Yang and Chang, which includes the ordinary least squares (OLS), the generalized ridge (GR) and the generalized Liu (GL) estimators, as special cases. Here, the performance of this new estimator is, theoretically, investigated over the OLS

A note on the computer generation of the binomial exponential distribution and generalizations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201201
P. JodráAbstract The binomial exponential distribution as well as its generalizations power binomial exponential and generalized binomial exponential have been introduced in the statistical literature as more flexible models than other probability distributions with support on the positive real line. Different algorithms have been proposed to generate by computer random samples from these new distributions

Regression analysis of the illnessdeath model with a shared frailty when all transition times are interval censored Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201201
Jinheum Kim; Jayoun Kim; Seong W. KimAbstract In biomedical or clinical studies, semicompeting risks data are often encountered in which one type of event may censor an other event, but not vice versa. An illnessdeath model is proposed to analyze these semicompeting risks data in the presence of interval censoring on both intermediate and terminal events. The Cox proportional hazards model is employed with a frailty effect to incorporate

New nonisomorphic detection methods for orthogonal designs Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201201
Xiao Ke; KaiTai Fang; A.M. Elsawah; Yuxuan LinAbstract Two fractional factorial designs are called isomorphic if one can be obtained from the other by relabeling the factors, reordering the runs and switching the levels of factors. Given a set of all orthogonal designs (ODs) with n runs, q levels and s factors, it may have several nonisomorphic subclasses. Once a new OD with this design size is generated, it is interesting to know which subclass

CUSUM control schemes for monitoring Wiener processes Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201201
Mengmeng Zhan; Liping LiuAbstract Detecting the abnormal degradation rate of Wiener processes is important for quality control and reliability management. In this paper, we propose upperside CUSUM control schemes for monitoring the abnormal degradation rate of three classes of Wiener processes. Average run length and standard deviation run length are applied to evaluating the efficiency of CUSUM control schemes. The results

Logextended exponentialgeometric parameters estimation using simple random sampling and moving extremes ranked set sampling Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201130
Rui Yang; Wangxue Chen; Yanfei DongAbstract In this paper, estimation of parameters α and β for the logextended exponentialgeometric distribution will be respectively considered in cases when β is known and when both are unknown. Simple random sampling (SRS) and moving extremes ranked set sampling (MERSS) will be used, and several traditional estimators will be considered. The estimators using MERSS are compared to the corresponding

Extropy based on records for random variables representing residual life Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201130
E. I. Abdul Sathar; Jitto JoseAbstract In this work, the residual extropy for measuring uncertainty involved in the remaining lifetime of a component has been investigated based on records. Some intriguing properties based on residual extropy of records have been discussed. The study also proposes a simple estimator for residual extropy of records and the performance of the proposed estimator has been evaluated as well. A detailed

Nested crossvalidation with ensemble feature selection and classification model for highdimensional biological data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201129
Yi Zhong; Prabhakar Chalise; Jianghua HeAbstract In recent years, application of feature selection methods in biological datasets has greatly increased. By using feature selection techniques, a subset of relevant informative features is obtained which results in more interpretable model improving the prediction accuracy. In addition, ensemble learning can further provide a more robust model by combining the results of multiple statistical

pVariation of CUSUM process and testing change in the mean Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201126
Tadas Danielius; Alfredas RačkauskasAbstract We propose and investigate a new test of model instability in the mean. The test is based on pvariation of stepwise CUSUM process. We establish a limiting distribution of the test statistics under null as well as under contiguous alternative.

Semidefinite programming based community detection for nodeattributed networks and multiplex networks Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201117
Fengqin Tang; Chunning Wang; Jinxia Su; Yuanyuan WangAbstract Community detection is an effective exploration technique for analyzing networks. Most of the network data not only describes the connections of network nodes but also describes the properties of the nodes. In this paper, we propose a community detection method collects relevant evidences from the information of node attributes and the information of network structure to assist the community

A broader class of modified twostage minimum risk point estimation procedures for a normal mean Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201117
Jun Hu; Yan ZhuangAbstract In this paper, we design an innovative and general class of modified twostage sampling schemes to enhance double sampling and modified double sampling procedures. Under the squared error loss plus linear cost of sampling, we revisit the classic problem of minimum risk point estimation (MRPE) for an unknown normal mean μ ( ∈ R ) when the population variance σ 2 ( ∈ R + ) also remains unknown

Empirical likelihood for mean difference between two samples with missing data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20201117
Yanhua Wu; Yufeng ShiAbstract In this paper, we consider the empirical likelihood confidence intervals for mean difference under the assumption of missing at random (MAR). We prove that the empirical loglikelihood ratio of mean difference converges to a standard chisquared distribution. We conduct simulation study to compare the proposed empirical likelihood with the normal approximation method in terms of the coverage