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Some simulation/computation in multivariate linear models of scale mixtures of skewnormalCauchy distributions Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200807
Fereshte Kahrari; Reinaldo Boris ArellanoValle; Clecio da Silva Ferreira; Diego GallardoIn this work, we extend standard likelihoodbased procedures to the multivariate linear model using the scale mixtures of multivariate skewnormalCauchy distributions. A simple EM algorithm for iteratively computing maximum likelihood estimates is derived. The observed information matrix is computed analytically to account for standard errors. Some results are obtained from real and simulated datasets

On the calibration of fractional twofactor stochastic volatility model with nonLipschitz diffusions Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200807
Farshid Mehrdoust; Somayeh FallahIn this work, the volatility processes of the double Heston model are extended to treat the long memory property of the volatility. In this article, we study our presented model as a new version of the twofactor stochastic volatility model that it is proposed to model the volatility by a mean reverting equation driven by fractional Brownian motion named fractional Cox–Ingersoll–Ross process. Next

Performance evaluation of different computational methods to estimate Wood’s lactation curve by nonlinear mixedeffects models Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200806
Luciana Carla Chiapella; María del Carmen GarciaNonlinear mixedeffects models allow modeling repeated measures over time. The fixed effects of these models allow incorporating covariates, whereas the random effects reflect the multiple sources of heterogeneity and correlation between and within the units. To estimate the parameters of these models, it is necessary to use iterative processes, which can be done through different approaches, some

Comparing ordinary ridge and generalized ridge regression results obtained using genetic algorithms for ridge parameter selection Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200805
Barnabe Ndabashinze; Gülesen Üstündağ ŞirayRidge regression is an alternative to the ordinary least squares method when multicollinearity presents among the regressor variables in multiple linear regression analysis. The selection of the ridge parameter is an important issue to obtain a good performance of the ridge regression. In this article, a new method is proposed for determining the ridge parameter in ridge regression. This method is

New ridge estimators in the inverse Gaussian regression: Monte Carlo simulation and application to chemical data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200804
Muhammad Amin; Muhammad Qasim; Saima Afzal; Khalid NaveedAbstract In numerous application areas, when the response variable is continuous, positively skewed, and well fitted to the inverse Gaussian distribution, the inverse Gaussian regression model (IGRM) is an effective approach in such scenarios. The problem of multicollinearity is very common in several application areas like chemometrics, biology, finance, and so forth. The effects of multicollinearity

Estimating the wrapped stable distribution via indirect inference Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200803
Marco BeeWe develop a constrained indirect inference approach based on a wrapped skewedt auxiliary model for the estimation of the wrapped stable distribution. To improve the finitesample properties of the estimators, we devise a bootstrapbased estimate of the weighting matrix employed in the indirect inference program. The simulation study suggests that, in terms of rootmeansquarederror, the indirect

Identifying parent locations in the NeymanScott process using Delaunay triangulation Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200801
Nader Najari; Mohammad Q. Vahidi Asl; Abdollah JalilianIn a clustered point pattern, consisting of parent and offspring points, in order to study the attributes of parent points as well as the behavior of offspring points which are scattered around the parents, one needs to know the locations of parent points generating offsprings. In this paper we use the Delaunay triangulation and present a general method to detect parent locations. The performance of

General least product relative error estimation for multiplicative regression models with or without multiplicative distortion measurement errors Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200731
Huili Zhou; Jun ZhangWe consider the parameter estimation for multiplicative linear regression models with or without multiplicative distortion measurement errors. For the latter, both the response variable and the covariates are are unobserved and distorted by unknown functions of a commonly observable confounding variable. With or without distortion measurement errors, we propose the general least product relative error

Estimating the proportion of true null hypotheses with application in microarray data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200731
Aniket BiswasA new formulation for the proportion of true null hypotheses ( π 0 ) , based on the sum of all pvalues and the average of expected pvalues under the false null hypotheses has been proposed in the current work. This formulation of the parameter of interest π 0 has also been used to construct a new estimator for the same. The proposed estimator removes the problem of choosing tuning parameters in the

Standardized likelihood ratio test for homogeneity of variance of several normal populations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200730
Esra GökpınarIn this study, we propose a standardized likelihood ratio test for homogeneity of variances of several normal populations. We obtain the exact mean and variance of the likelihood ratio test. The proposed test, which does not require intensive simulation, is quite easy to implement especially for researchers. Furthermore, we derive the null distribution of the likelihood ratio test via Monte Carlo simulation

A change point control chart for monitoring the production lead time based on sum of squared ranks Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200730
Jianlan Zhong; Xulong Hu; Tian JiangAssuming that a twostage serialparallel processing system is an M/G/m queuing system. Firstly, let sojourn time of the order in one stage is the system state, and the state space equation is used to model this system. Also, the influence of the mean shift of sojourn time on the downstream stage is analyzed. Secondly, the average sojourn time is calculated. And the distribution function of the sojourn

Twoparameter estimator for the inverse Gaussian regression model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200730
Muhammad Nauman Akram; Muhammad Amin; Muhammad AmanullahThe inverse Gaussian regression model (IGRM) is frequently applied in the situations, when the response variable is positively skewed and well fitted to the inverse Gaussian distribution. The maximum likelihood estimator (MLE) is generally used to estimate the unknown regression coefficients of the IGRM. The performance of the MLE method is better if the explanatory variables are uncorrelated with

Regression analysis of dependent current status data with the accelerated failure time model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200730
Da Xu; Shishun Zhao; Jianguo SunIn this article, we discuss the regression analysis of dependent current status data under the accelerated failure time model. There exist many literatures discussing the regression analysis of current status data under different models, but few literature discussing the regression problem of dependent current status data under the AFT model. Corresponding to this, we propose a sieve maximum likelihood

A novel robust control chart for monitoring multiple linear profiles in phase II Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200728
Mohammad Mahdi Ahmadi; Hamid Shahriari; Yaser SamimiA profile is a relationship between the response variable(s) and the independent variable(s), which describes the quality of a process or product. The profile can be monitored by a process control chart, which is an important tool in the statistical process control. Using the robust estimators in monitoring profiles in the presence of contamination is so effective, and it improves the efficiency of

Modeling basketball games by inverse Gaussian processes Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200726
Xinyu Tian; Yiran Gao; Jian ShiThe scoring processes of home and away team in basketball games are modeled by two dependent inverse Gaussian processes with a teamspecific parameter that measures the team strength. A common latent variable that measures the game pace is designed to characterize the dependence. A moment estimation method combined with maximum likelihood estimation is proposed to fit the parameters and a Bayesian

A novel approach to monitor simple linear profiles using individual observations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200725
Abdul Haq; Mehwish Bibi; Burhan Ali ShahIn some quality control applications, quality of a product or process may be characterized effectively by a functional relationship between one or more variables that is typically referred to as a profile. In this paper, we propose four control charts, namely the maximum CUSUM (MaxCUSUM), maximum Crosier CUSUM (MaxCCUSUM), maximum EWMA (MaxEWMA) and maximum double EWMA (MaxDEWMA) charts, for monitoring

Sparse local influence analysis Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200725
Jun Lu; Lei ShiThe local influence analysis is useful for identifying influential observations in statistical diagnostics and sensitivity analysis. However, it is often criticized for lack of a rigorous criterion to judge the influence magnitude from the elements of the main diagnostic. In this paper, a new method, call sparse local influence analysis, is proposed to detect the influential observations. We establish

Spatiotemporal procedures for the statistical surveillance of spatial autoregressive models with heavy tails Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200723
Robert Garthoff; Philipp OttoThe purpose of this article is the statistical surveillance of spatial autoregressive models, where the observed process is monitored over both space and time. The considered spatial model contains disturbances with heavy tails. The control procedures based on exponential smoothing or cumulative sums are constructed using characteristic quantities including the first and the second moments to monitor

Gaussian copulabased zeroinflated power series joint models to analyze correlated count data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200721
Fatemeh Rezaee; Ehsan Bahrami Samani; Mojtaba GanjaliA Gaussian Copulabased regression model is proposed that accounts for associations between count responses with extra zeros. Our approach entails underlying latent variables to indicate the latent mechanisms which generate the count responses where some of the count responses are inflated in a zero point. The model contains, as special submodels, several important distributions such as the power

On comparing locations of twoparameter exponential distributions using sequential sampling with applications in cancer research Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200721
Yan Zhuang; Sudeep R. BapatIn this paper, we develop testing procedures for hypotheses regarding the difference of location parameters from two independent twoparameter exponential populations with unknown but proportional scale parameters. We design appropriate twostage and purely sequential procedures to determine the appropriate sample sizes while controlling both typeI and typeII error probabilities at or below the preassigned

The asymptotic distribution of CUSUM estimator based on αmixing sequences Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200720
Min Gao; Saisai Ding; Shipeng Wu; Wenzhi YangIn this paper, we consider the CUSUM estimator based on αmixing sequences. By giving the consistency estimators for mean and covariance functions, the limit distribution of CUSUM estimator is presented as a standard Brownian bridge, which can be used in the change point detection. As applications, some simulations and real data examples are illustrated to test our CUSUM estimator.

Isolating changed panels and estimating common change point after sequential detection with FDR control Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200720
Yanhong WuQuick detection of common changes is critical in sequential monitoring of multistream data where a common change is referred as a change that only occurs in a portion of panels. After a common change is detected by using a SRSUM procedure, we compare the CUSUM and EWMA methods in terms of FDR and FNR after a common change is detected. The BHtype procedure is used for both CUSUM processes and EWMA

The revisited knockoffs method for variable selection in L 1penalized regressions Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200719
Anne GégoutPetit; Aurélie GueudinMuller; Clémence KarmannWe consider the problem of variable selection in regression models. In particular, we are interested in selecting explanatory covariates linked with the response variable and we want to determine which covariates are relevant, that is which covariates are involved in the model. In this framework, we deal with L 1penalized regression models. To handle the choice of the penalty parameter to perform

An efficient algorithm for sampling from sink (x) for generating random correlation matrices Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200719
Enes Makalic; Daniel F. SchmidtIn this note, we develop a novel algorithm for generating random numbers from a distribution with a probability density function proportional to sin k(x),x∈(0,π) and k≥1. Our algorithm is highly efficient and is based on rejection sampling where the envelope distribution is an appropriately chosen beta distribution. An example application illustrating how the new algorithm can be used to generate

Graphical analysis of residuals in multivariate growth curve models and applications in the analysis of longitudinal data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200716
Jemila S. Hamid; Wei Liang Huang; Dietrich von RosenStatistical models often rely on several assumptions including distributional assumptions on outcome variables as well as relational assumptions representing the relationship between outcomes and independent variables. Model diagnostics is, therefore, a crucial component of any model fitting problem. Residuals play important roles in model diagnostics and checking assumptions. In multivariate models

Modelfree survival conditional feature screening Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200713
Xiaolin Chen; Wei Liu; Xiaojing ChenIn many largescale survival studies, some features are known to be truly predictive of the survival time among massive covariates in advance. To make full use of this prior information, we propose a new modelfree conditional feature screening procedure by extending the MC screening method via inverse probability censoring weighting. The newly suggested method has at least three appealing merits:

A bivariate generalized linear exponential distribution: properties and estimation Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200711
A. K. Pathak; P. VellaisamyThis article introduces a new family of bivariate generalized linear exponential (BGLE) distributions, whose marginals are generalized linear exponential (GLE) distributions. We derive the expressions for regression function, product moments and the stress–strength parameter P ( X < Y ) for the BGLE distribution. Several statistical properties, a local dependence function and some association measures

Tweedie gradient boosting for extremely unbalanced zeroinflated data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200711
He Zhou; Wei Qian; Yi YangTweedie’s compound Poisson model is a popular method to model insurance claims with probability mass at zero and nonnegative, highly rightskewed distribution. In particular, it is not uncommon to have extremely unbalanced data with excessively large proportion of zero claims, and even traditional Tweedie model may not be satisfactory for fitting the data. In this paper, we propose a boostingassisted

Penalty, post pretest and shrinkage strategies in a partially linear model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200711
Siwaporn Phukongtong; Supranee Lisawadi; S. Ejaz AhmedWe addressed the problem of estimating regression coefficients for partially linear models, where the nonparametric component is approximated using smoothing splines and subspace information is available. We proposed pretest and shrinkage estimation strategies using the profile likelihood estimator as the benchmark. We examined the asymptotic distributional bias and risk of the proposed estimators

A family of densityhazard distributions for insurance losses Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200710
S. A. Abu Bakar; S. Nadarajah; N. NgatamanWe propose a family of distributions as an alternative for a recent compound unimodal distribution for modeling insurance losses. The family of distributions, referred to as densityhazard distributions, has closed form density and distribution functions, hence easier to fit and simulate from. The distributions also show good adherence to insurance loss data and estimates risk measures relatively closely

Exact tests for outliers in Laplace samples Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200709
ChienTai Lin; Narayanaswamy Balakrishnan; Man Ho LingThe exact null distributions of test statistics used for testing up to k ( ≥ 1 ) upper outliers in a twoparameter Laplace sample are investigated. Two types of test statistics, namely, the modified Murphy’s test for k upper normal outliers and the general Dixontype test statistic discussed by Childs, are considered. Utilizing the result of conditional independence of blocked ordered data established

A gammafrailty model for intervalcensored data with dependent examination times: a computationally efficient approach Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200709
ChyongMei Chen; Paosheng Shen; TingHsuan LeeIntervalcensored survival data arise naturally in many fields such as medical followup studies, in which the event or failure is not observed exactly but only known to occur within a time interval. Most existing approaches for analyzing intervalcensored failure time data assume that the examination times and the failure time are independent or conditionally independent given covariates. While this

Varianceestimationfree test of significant covariates in highdimensional regression Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200709
Kai Xu; Zhiling ShenIn a highdimensional linear regression model, this article is concerned with testing statistical significance of a subset of regression coefficients. The conventional partial Ftest is not applicable in highdimensional situations. Several methods for testing whether any of the discarded covariates is significant conditional on relative importance of predictors have been proposed in the recent literature

An improved randomized response model for simultaneous estimation of means of two quantitative sensitive variables Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200709
Amod Kumar; Gajendra K. Vishwakarma; G. N. SinghWe propose an improved randomized response model for the simultaneous estimation of population means of two quantitative sensitive variables by using blank card option that make use of one scramble response and another fake response. The properties of the proposed estimator have been analyzed and empirical studies are performed to show its dominance over existing estimators along with enhance privacy

Model free feature screening for ultrahigh dimensional covariates with right censored outcomes Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Fengli Song; Peng Lai; Baohua Shen; Lianhua ZhuThis paper is concerned with feature screening for the ultrahigh dimensional survival data. We propose a new feature screening procedure by extending the method of Zhu et al. via inverse probability censoring weighting. The proposed procedure enjoys two appealing merits. First, it does not need to specify any model assumption between the response and the covariates. Thus, it is robust to the model

Reliability and capacity evaluation of multiperformance multistate weighted K −outofn systems Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Xinchen Zhuang; Tianxiang Yu; Zhongchao Sun; Kunling SongSystems in engineering usually not only have multiple states but also may be required to fulfill several functions, meaning the system has multiple performance variables in a specific state. In a multiperformance multistate weighted K −outofn system, the system consists of several multiperformance multistate components for completing its different functions. As this system is more complicated

Note on the family of proportional reversed hazard distributions Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Jung In Seo; Yongku KimFor many years, authors have been interested in developing methods for generating distributions that provide a flexible family to model lifetime variables. This paper proposes an exact inference approach to the family of proportional reversed hazard distributions based on the pivotal quantity, which yields exact confidence intervals with the shortestlength as well as reasonable estimators for the

A simulationbased comparison of two methods for determining the treatment effect in children diagnosed with hyperkinetic disorder Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200501
Maria Iachina; Peter MorlingIn order to show the effect of treatment, the change between two repeated psychometric measurements at the individual level should be estimated. The simplest method is to calculate the absolute difference between two measurements. However, measurements obtained in a clinical setting are often influenced by other changes not related to the treatment. One of the typical sources of error is regression

A dynamic delaybased reliability evaluation model for communication networks Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200501
Jian Shi; Yixuan Meng; Shaoping Wang; Zongxia JiaoThe traditional network reliability research generally focuses on the topological connectivity among given nodes based on graph theory. Without considering the relationship between network failures and network performance degradation, traditional network reliability analysis cannot reflect actual network ability reaching up to a certain accomplishment level over a utilization interval. As for the performability

A double moving average control chart: Discussion Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Vasileios Alevizakos; Kashinath Chatterjee; Christos Koukouvinos; Angeliki LappaA double moving average (DMA) control chart has been proposed in the literature for monitoring the process mean. Several studies have also been done based on this scheme. Unfortunately, the variance of DMA statistic that has been used in these studies is not correct. In this article, we provide the correct variance of the DMA chart and through a simulation study, we evaluate its performance. It is

Mean estimation with generalized scrambling using twophase sampling Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Aamir Sanaullah; Iram Saleem; Sat Gupta; Muhammad HanifSousa et al. and Gupta et al. presented ratio and regression estimators of population mean of a sensitive variable using auxiliary information in simple random sampling without replacement. In this article, we propose a generalized randomized response technique (RRT) model and use it to develop some exponential estimators in twophase sampling. We also discuss the privacy protection level of the proposed

Performance of maximum EWMA control chart in the presence of measurement error using auxiliary information Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Muhammad NoorulAmin; Amjad Javaid; Muhammad Hanif; Eralp DoguEWMA and MaxEWMA charts are considered efficient for individual as well as joint monitoring of mean and variance shifts in the production process. However, measurement error is affecting the efficiency of these charts. In this study we propose a maximum exponentially weighted moving average with measurement error using auxiliary information control chart and name it MaxEWMAMEAI control chart. The

Ratio and regression type estimators of a new measure of coefficient of dispersion Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Christin Variathu Eappen; Stephen A. Sedory; Sarjinder SinghIn this article, we first discuss a few properties along with limitations of traditional measure of coefficient of dispersion in comparison to the wellknown standard measure of variation called the coefficient of variation. To overcome the limitations in the traditional coefficient of dispersion, a new measure of coefficient of dispersion is introduced which is more informative than the conventional

Ridge regression and the Lasso: how do they do as finders of significant regressors and their multipliers? Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Rajaram GanaA simulation study is done to compare Ridge Regression (RR) and the Lasso, under the assumption of a linear model, by calculating four metrics: the squared distance, from the true coefficients, of estimated coefficients that are both statistically significant and true; the proportion of true regressors discovered; the squared distance, from the true predictions, of the predictions made using the estimated

The information domain confidence intervals in univariate linear calibration Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Guimei Zhao; Xingzhong XuWe consider the confidence interval for the univariate linear calibration, where a response variable is related to an explanatory variable by a simple linear model, and the observations of the response variable and known values of the explanatory variable are used to make inferences on a single unknown value of the explanatory variable. Since the univariate linear calibration suffers from a problem

Analysis of longitudinal ordinal data using semiparametric mixed model under missingness Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Subrata Rana; for the Alzheimer’s Disease Neuroimaging InitiativeIn studies related to social or medical sciences, ordinal responses are often recorded repeatedly over time on a subject. A semiparametric model with spline smoothing has been considered to capture the temporal trend exhibited in the longitudinal data. In addition, information on covariates and/or responses may not be available in one or more visit. A dynamic model for both missing responses and covariates

Bootstrap inference on variance component functions in the unbalanced twoway random effects model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
RenDao Ye; WenTing Ge; Kun LuoIn this paper, we consider the onesided hypothesis testing problems for variance component functions in the unbalanced twoway random effects model. Firstly, using the Bootstrap approach and generalized approach, the test statistics and confidence intervals for the sum and ratio of variance components are constructed respectively. Next, the Monte Carlo simulation results indicate that the Bootstrap

Inferences on location parameter in multivariate skewnormal family with unknown scale parameter Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200707
Ziwei Ma; YingJu Chen; Tonghui Wang; Jing LiuIn this paper, the statistical inference on the location parameter vector is studied in the multivariate skewnormal model with unknown scale parameter and known shape parameter. Based on the distribution of the generalized Hotelling’s T 2 statistic, confidence regions and hypothesis tests on the location parameter, μ , are obtained. The power function of the test is studied numerically as well. For

Shannon’s entropy of partitions determined by hierarchical clustering trees in asymmetry and dimension identification Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200706
J. S. Corredor; A. J. QuirozIn the multivariate statistics community, it is commonly acknowledged that among the hierarchical clustering tree (HCT) procedures, the single linkage rule for intercluster distance, tends to produce trees which are significantly more asymmetric than those obtained using other rules such as complete linkage, for instance. We consider the use of Shannon’s entropy of the partitions determined by HCTs

A new method of testing for a unit root in the INAR(1) model based on variances Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200706
Fuming Lin; Daimin ShiWe present a new method of testing for unit roots in the INAR(1) model based on estimated variances. We present detailed simulation evidence regarding the performance of the new test statistics that show that our method is more powerful than the Dickey–Fuller tests especially in nearly unit root circumstances. We evaluate the presence of a unit root in two empirical time series, namely, the number

The exponentiated power exponential semiparametric regression model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200706
Fábio Prataviera; Edwin M. M. Ortega; Gauss M. Cordeiro; Vicente G. CanchoWe propose a new semiparametric regression model with exponentiated power exponential errors using the Bspline basis for nonlinear effects. We adopt the framework of the generalized additive models for location, scale, and shape to fit this regression model. We obtain the maximum penalized likelihood estimates of the model parameters by considering nonlinear effects. Some globalinfluence measurements

On some periodic INARMA(p,q) models Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200705
Mohamed Bentarzi; Nawel AriesThis paper deals with the study of some probabilistic and statistical properties of some particular models of the class of Periodic IntegerValued Autoregressive Moving Average, PINARMA(p, q), Models. For any considered particular model, the necessary and sufficient conditions for the periodically stationary in the first and second order, are established. The closed forms of the mean and the variance

Nonparametric independence feature screening for ultrahighdimensional missing data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200705
Jianglin FangMissing data are common in medical and social science studies and often face a serious challenge in ultrahighdimensional data analysis. In this paper, a nonparametric feature screening approach based on the imputation technique is proposed for ultrahighdimensional data with responses missing at random, where the imputation technique is used to replacing each missing value with a set of plausible

An artificial bee colony algorithm for mixture modelbased clustering Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200705
Anthony E. Culos; Jeffrey L. Andrews; Hamid AfshariFinite mixture modelbased clustering is a popular method to perform unsupervised learning, however the classic approach for parameter estimation, the expectationmaximization algorithm, is quite susceptible to converging to, at times extremely poor, local maxima. Recently, swarmbased algorithms have gained popularity in a number of optimization scenarios. One such swarmbased optimization procedure

An optimized conformable fractional nonhomogeneous gray model and its application Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200704
Wanli Xie; WenZe Wu; Tao Zhang; Qi LiDeveloping a robust, accurate forecasting model and improving the prediction abilities of the limited historical data that lacks statistical rules has become a top priority. To address this problem, an improved conformable fractional nonhomogeneous gray model, namely CFONGM(1,1,k,c), is proposed. Combining the dynamic backgroundvalue and particle swarm optimization algorithm to further improve forecasting

A class of hybrid type estimators for variance of a finite population in simple random sampling Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200702
Aamir Sanaullah; Iqra Niaz; Javid Shabbir; Iqra EhsanIn this paper, a class of hybrid type estimators is proposed in estimating the finite population variance using single auxiliary variable in simple random sampling. Expressions for the bias and the mean square error (MSE) are derived up to the first order of approximation. Theoretically comparisons are provided to show that the proposed estimators perform more efficiently than various existing estimators

Estimation of the parameters of multivariate stable distributions Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200702
Aastha M. Sathe; N. S. UpadhyeIn this paper, we first discuss some of the wellknown methods available in the literature for the estimation of the parameters of a univariate/multivariate stable distribution. Based on the available methods, a new hybrid method is proposed for the estimation of the parameters of a univariate stable distribution. The proposed method is further used for the estimation of the parameters of a strictly

Robust estimation and outlier detection for varyingcoefficient models via penalized regression Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200702
Guangren Yang; Sijia Xiang; Weixin Yao; Lin XuVaryingcoefficient models (VCMs) are widely used in a variety of statistical applications. However, the classical VCMs based on least squares are prone to the presence of even a few severe outliers. In this article, a mean shift parameter is added for each observation to reflect outliers, and different penalties are then applied to the shift parameters to get sparse estimates. The jointly penalized

Exact probability of fixed patterns occurring in a random sequence Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200630
KeNing Sheng; Joseph I. NausWe derive a procedure to obtain the exact probability that a specific pattern of letters occurs in a longer random sequence of letters. The procedure is generalized to find the exact probability of a fixed (specific) single pattern, and a union or intersection of multiple fixed (specific) patterns within a random sequence perfectly for any distributions of a cell in the random sequence, and can handle

Combined attrivari inspection policy for resubmitted lots based on the process capability index Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20200630
S. BalamuraliIn this paper, an attrivari inspection policy for the resubmitted lots based on the process capability index Cpk is proposed. The proposed sampling plan comprises of both attribute and variables inspections for the resubmitted lots using single sampling plan. In the case of variables inspection, both symmetric and asymmetric fraction non conforming cases have been considered. Tables are developed