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On modified unbiased twoparameter estimator Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210509
Mustafa Nadhim Lattef, Mustafa I. AlheetyAbstract The ridgetype estimators have been intensively studied and modified for the linear regression model. In this article, we introduce a modified unbiased twoparameter estimator (MUTPE) as a new estimator to solve the multicollinearity problem for the linear regression model. The MUTPE has been obtained as a linear combination of unbiased twoparameter estimator (UTPE). We give a simulation

Bayesian multilevel multidimensional item response modeling approach for multiple latent variables in a hierarchical structure Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210509
Jiwei Zhang, Jing Lu, Xin Xu, Jian TaoAbstract In this paper, we propose a multilevel multidimensional item response model for studying the relations among multiple abilities and covariates in a hierarchical data structure. As an example, this study is well suited to examining the scenario in which a test measures multidimensional latent traits (e.g., reading ability, cognitive ability, and computing ability) and in which students are

Rejection rates of bootstrapped and exact heteroskedasticity tests in response to skedastic function and normal or skewed disturbances Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210506
Václav Adamec, Eric P. SmithAbstract This study focuses on examining the power of bootstrapped and exact tests to detect variance heterogeneity in linear models via Monte Carlo. Three independent MC schemes were set up corresponding to distinct forms of heteroskedasticity, degree of heteroskedasticity and with or without normality. The tests produced unequal power in the simulated combinations; some performed poorly, when exposed

Full conditional distributions for Bayesian multilevel models with additive or interactive effects and missing data on covariates Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210506
Roy Levy, Craig K. EndersAbstract Missing data are a common occurrence in analyses of multivariate data, including in multilevel modeling. Bayesian approaches to handling missing data in multilevel modeling have garnered increasing attention, either on their own or in service of multiple imputation. However, these applications are largely confined to specific models or missingness patterns. The current work provides a coherent

Bootstrap Liu estimators for Poisson regression model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210506
Ismat Perveen, Muhammad SuhailAbstract The Liu estimator is used to get precise estimatesby introducing bootstrap technique to reduce the problem of multicollinearity in Poisson regression model. In the presence of multicollinearity, the variance of maximum likelihood estimator (MLE) becomes overstated and theinference based on MLEdoes not remain trustworthy. In this article, we proposed some new Poisson bootstrap Liu and ridge

Bayesian approach to the metaanalysis of multicategory prevalence Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210226
Esin AvciAbstract Multiple category prevalence represents the prevalence of the specific disease with different kcategory (k > 2) statuses, such as mild, moderate and severe. This study proposed the Bayesian method for the metaanalysis of studies with multiple category prevalence. The Dirichletmultinomial model was used to obtain the Bayesian approach. In this way, both the opportunity to consider the preinformation

Logarithmic confidence intervals for the crossproduct ratio of binomial proportions under different sampling schemes Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210502
Chanakan Sungboonchoo, SuFen Yang, Wararit Panichkitkosolkul, Andrei VolodinAbstract We consider the problem of logarithmic interval estimation for a crossproduct ratio ρ=p1(1−p2)p2(1−p1) with data from two independent Bernoulli samples. Each sample may be obtained in the framework of direct or inverse Binomial sampling schemes. Asymptotic logarithmic confidence intervals are constructed under different types of sampling schemes, with parameter estimators demonstrating exponentially

Normality and significance testing in simple linear regression model for large sample sizes: a simulation study Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210502
Xavier Javines BilonAbstract Data analysis techniques that rely on standard statistical tools and algorithms often encounter problems when dealing with data sets that have large sample sizes. In this study, two statistical tests done in conducting simple linear regression analysis were revisited. In particular, the study simulated the effects of large sample sizes and amount of contamination in the data due to nonsampling

Inference for the stressstrength reliability for the twoparameter Burr type X under Type I and Type II censoring Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210428
Li Luo, James G. SurlesAbstract In this paper, asymptotic normal, approximate Fbased and BCa bootstrap inferences are proposed for the stressstrength reliability model P(X

Spacetime border analysis to evaluate and detect clusters Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210427
A. R. Duarte, S. B. Silva, F. L. P. Oliveira, A. C. L. Almeida, L. H. DuczmalAbstract This article presents a statistic to identify the proportional intensity of an area to belong to a spacetime cluster, which is very important in spatial statistics. A significant extension of the socalled Ffunction has been described in detail, which, associated with new technology, allows the evaluation of the intensity of change moments in the time process and outline possible occurrences

On partial and conditional association measures for ordinal contingency tables Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210426
Zheng Wei, Daeyoung KimAbstract This paper proposes new nonmodel based partial/conditional association measures for ordinal contingency table where the main interest is the association between two ordinal variables of interest, adjusting for the effect of the covariate(s). We first develop a new type of scores for the ordinal variables, subcopula scores, taking into account the ordering of the categories of the variables

Comparison of estimators and predictors based on modified weibull records: Bayesian and nonBayesian approaches Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210425
Mohammed S. Kotb, Mohammad Z. RaqabAbstract Based on record statistics from threeparameter modified Weibull distribution, we consider the problem of estimating the unknown parameters using Bayesian and nonBayesian approaches. Under a continuousdiscrete joint prior distribution, Bayesian estimators and confidence intervals for the shape and scale parameters involved in the underlying model are obtained. In addition, maximum likelihood

A mixture binary RRT model with a unified measure of privacy and efficiency Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210424
Maxwell Lovig, Sadia Khalil, Sumaita Rahman, Pujita Sapra, Sat GuptaAbstract In this study, we introduce a mixture binary Randomized Response Technique (RRT) model by combining the elements of the Greenberg Unrelated Question model and the Warner Indirect Question model. We also account for untruthful responding in the proposed model. A unified measure of model efficiency and respondent privacy is also presented. Finally, we present a simulation study to validate the

GPAbin: unifying visualizations of multiple imputations for missing values Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210424
J. NienkemperSwanepoel, N. J. le Roux, S. GardnerLubbeAbstract Multiple imputation is a wellestablished technique for analyzing missing data. Multiple imputed data sets are obtained and analyzed separately using standard complete data techniques. The estimates from the separate analyses are then combined for the purpose of statistical inference. However, the exploratory analysis options of multiple imputed data sets are limited. Biplots are regarded

Comparison of tests for association of 2 × 2 tables under multiple testing setting Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210422
Huan Cheng, Jianghua HeAbstract Fisher’s exact test and Pearson’s chisquared test are frequently used for testing associations of two binary variables in 2by2 contingency tables. In the single test setting, many studies have shown that the asymptotic Pearson’s chisquared test cannot preserve the test size for small samples and Fisher Exact test tends to be overly conservative. Multiple unconditional exact tests were

Reliability inference for stressstrength model based on inverted exponential Rayleigh distribution under progressive TypeII censored data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210422
Jin’ge Ma, Liang Wang, Yogesh Mani Tripathi, Manoj Kumar RastogiAbstract In this paper, stressstrength model is studied for an inverted exponential Rayleigh distribution (IERD) when the latent failure times are progressively TypeII censored. When both strength and stress random variables follow common IERD scale parameters, the maximum likelihood estimate of stressstrength reliability (SSR) is established and the associated approximate confidence interval is

Applications of covering principle to clinical trials with multiple objectives Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210422
Hong Zhou, Huajiang LiAbstract A novel covering principle was recently proposed to address the multiplicity issue in the multiple hypotheses testing with a hierarchical structure. In this article, a sieve method is proposed to decompose the family of hypotheses into subfamilies. A Shiny web app in R is developed which makes it easier for users to build the objectivetailored multiple testing procedure based on the covering

Krylov subspace solvers for ℓ1 regularized logistic regression method Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210419
M. El Guide, K. Jbilou, C. Koukouvinos, A. LappaAbstract In this paper, we propose an approach based on Krylov subspace methods for the solution of ℓ1 regularized logistic regression problem. The main idea is to transform the constrained ℓ2 ℓ1 minimization problem obtained by applying the IRLS method to a ℓ2 ℓ2 one that allow regularization matrices in the usual 2norm regularization term. The regularization parameter that controls the equilibrium

Bayesian model averaging in longitudinal studies using Bayesian variable selection methods Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210419
Belay Birlie Yimer, Martin Otava, Teshome Degefa, Delenasaw Yewhalaw, Ziv ShkedyAbstract Parameter estimation is often considered as a post model selection problem, i.e., the parameters of interest are often estimated based on “the best” model. However, this approach does not take into account that “the best” model was selected from a set of possible models. Ignoring this uncertainty may lead to bias in estimation. In this paper, we present a Bayesian variable selection (BVS)

A consistent method of estimation for threeparameter generalized exponential distribution Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210419
Kiran Prajapat, Sharmishtha Mitra, Debasis KunduAbstract In this article, we provide a consistent method of estimation for the parameters of a threeparameter generalized exponential distribution which avoids the problem of unbounded likelihood function. The method is based on a maximum likelihood estimation of the shape parameter, which uses location and scale invariant statistic, originally proposed by Nagatsuka et al. (A consistent method of

Efficient estimation in semiparametric selfexciting threshold INAR processes Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Mohamed Bentarzi, Mohamed SadounAbstract This paper focuses on the efficient estimation problem of a more realistic semiparametric SETINAR model of order one with two regimes based on binomial thinning operator. Unlike parametric framework, we do not suppose that the distribution of the innovation process belongs to a parametric family. Instead, the innovation distribution is totally unspecified and is supposed to satisfy only some

Classical and Bayesian inference on traffic intensity of multiserver Markovian queuing system Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Arpita Basak, Amit ChoudhuryAbstract In this paper we consider multiserver single queue system in which interarrival and service times are exponentially distributed. When assessing the performance of such queuing model, information regarding the parameter traffic intensity (ρ), also called the utilization factor of the service station, is very essential. The unknown factor ρ is therefore our parameter of interest in the present

Monitoring processes with ordinal data: an areabased approach Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Surajit Pal, Susanta Kumar GauriAbstract In case of an ordinal process ( p1, p2, p3) involving three ordered categories, always the following condition holds: p3≤(1−p1), where p1 and p3 are the proportion of items in the first and third categories respectively. Using this fact, Pal and Gauri (2019 Pal, S., and S. K. Gauri. 2019. Evaluating capability of a process with ordinal responses. Communication in Statistics – Simulation and

On weighted cumulative Tsallis residual and past entropy measures Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Siddhartha Chakraborty, Biswabrata PradhanAbstract In this work, we propose weighted cumulative Tsallis residual and past entropy measures and their dynamic versions. The properties of the proposed entropy measures are studied. It is shown that the residual entropy uniquely determines the survival function. Characterizations for Rayleigh and Power distributions are obtained based on the proposed entropy measures. The empirical versions of

Logistic regression model with TreeNet and association rules analysis: applications with medical datasets Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Pannapa ChangpetchAbstract This study establishes an innovative and effective approach for generating new variables and interactions for logistic regression using the two data mining techniques TreeNet and association rules analysis. With TreeNet as the first step in our logistic model building, the new variables are generated by discretizing the quantitative variables. With ASA as the following step, the new interactions

An efficient nonparametric double progressive mean chart for monitoring of the process location Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Zameer Abbas, Hafiz Zafar Nazir, Muhammad Riaz, Muhammad Abid, Noureen AkhtarAbstract A control chart has become a choice of quality practitioners for monitoring the output of industrial and production processes. It is a common practice to develop control charts under normality assumption or known distribution of the quality characteristic(s). These control charts are known as parametric charts. These charts may provide misleading results when the normality assumption of the

Forecasting overdispersed INAR(1) count time series with negative binomial marginal Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Manik Awale, Akanksha S. Kashikar, T. V. RamanathanAbstract This paper addresses the coherent forecasting problem for overdispersed integervalued autoregressive (INAR) model of order one having negative binomial marginal distribution. INAR models with Poisson or geometric marginal distribution have been used by several researchers to tackle the forecasting and related issues in low count time series. However, when the process results in relatively

Comparison of some interval estimation methods for the parameters of the gamma distribution Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210414
Edilberto Nájera, Addy BolívarCiméAbstract Several methods of finding interval estimators of the parameters of the gamma distribution are considered in the literature. In this work we compare the following methods: Wald confidence intervals; profile likelihood intervals; Bayesian intervals using the Jeffreys prior, the reference prior when α is the parameter of interest and β the nuisance parameter, the reference prior when β is the

GTL regression: a linear model with skewed and thicktailed disturbances Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210412
Wim Vijverberg, Takuya HasebeAbstract A maximum likelihood estimator of a linear regression model is efficient relative to the customary Ordinary Least Squares (OLS) estimator when disturbances are skewed and/or thicktailed. In order to model skewed and thicktailed disturbances, we specify a highly flexible Generalized Tukey Lambda (GTL) distribution that can closely mimic many other unimodal distributions. The GTLbased maximum

Cost model of variable multiple dependent state sampling plan with rectifying inspection Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210410
Rabia Arshad, Yasar Mahmood, Muhammad Aslam, Hina Khan, Nasrullah Khan, Naufil SakranAbstract It is always foreseen to upturn the efficiency of an acceptance sampling plan for lot sentencing. In this research, variable multiple dependent state sampling plan considering the quality cost with rectifying inspection is proposed for normally distributed quality characteristics. The staple objective is to curb the total cost of the lot under inspection and improve the quality of outgoing

Bias assessment in local regression Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210409
Wenkai Ma, W. John BraunAbstract Local polynomial regression is a convenient method for smoothing scatterplots with readily available software. However, it is well known that variable amounts of bias are induced by the smoothing operation. This article proposes a simple visualization tool based on approximate confidence intervals which can alert the data analyst to regions of the regression function domain which might be

Asymptotic standard errors of intraclass correlation coefficients for twoway model Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210409
Rashid S. Almehrizi, Mahmoud EmamAbstract Intraclass correlation coefficients are estimated using appropriate analysis of variance models that are commonly used in behavioral measurement, biometric, and psychometric. The ICCs estimation accuracy is quantified by the degree of their sampling variability using the asymptotic standard errors or confidence intervals, which facilitates conducting hypothesis testing on ICCs. The article

An adaptive weighted least squares ratio approach for estimation of heteroscedastic linear regression model in the presence of outliers Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210409
Zahra Zafar, Muhammad AslamAbstract The issue of heteroscedasticity and its adverse impact on the estimation of a linear regression model has been extensively discussed in the available literature. Some adaptive estimators have also been proposed in the context of weighted least squares (WLS) to address the issue. Estimation of linear regression model becomes more challenging when the issue of heteroscedasticity is bundled together

Sensitivity analysis for assumptions of general mediation analysis Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210408
Wentao Cao, Yaling Li, Qingzhao YuAbstract Mediation analysis is widely used to identify significant mediators and estimate the mediation (direct and indirect) effects in causal pathways between an exposure variable and a response variable. In mediation analysis, the mediation effect refers to the effect transmitted by mediator intervening the relationship between an exposure variable and a response variable. Traditional mediation

A generalized exponentially weighted moving average control chart for monitoring autocorrelated vectors Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210406
Binhui Wang, Zhifeng He, Lianjie ShuAbstract With recent advancement in automation and data acquisition technologies, a number of quality variables are often measured at high frequency and thus are likely to be autocorrelated. The multivariate exponentially weighted moving average (MEWMA) control chart with a scalar smoothing parameter has been widely suggested for monitoring autocorrelated vectors, owing to its simplicity. However,

Alex mean (AlM) location estimator for measure of Center of data Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210405
Alemu BekeleAbstract Computational data analysis is an essential component of modern statistics. The most important challenge in classical statistics was an estimation of the location parameter. In addition, the selection of estimators among potential estimators is another problem in the computational data analysis. Therefore, the main objective of this study is to develop a location estimator that has been modified

Maximum entropy and empirical likelihood in lengthbiased setting: a comparison study Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210405
Hussain A. A. Aleagobe, Vahid Fakoor, Sarah JomhooriAbstract This paper focuses on comparing two nonparametric methods of constructing confidence intervals: the maximum entropy (ME) and the empirical likelihood (EL). The objective is to estimate probability distributions given some moment conditions in the presence of lengthbiased sampling. Some simulation studies are conducted to indicate ME confidence intervals have better coverage probabilities

A generally weighted moving average t control chart for monitoring shifts in the process mean Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210405
Vasileios Alevizakos, Kashinath Chatterjee, Christos Koukouvinos, Angeliki LappaAbstract Control charts based on the sample mean X¯ are used for monitoring shifts in the process mean under the assumption that the process standard deviation is stable or well estimated. However, in many applications, this assumption is violated. In this case, t control charts are preferred as they are robust to changes in the process standard deviation. In this article, a generally weighted moving

Sample size determination for a Bayesian costeffectiveness model with structural zero costs Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210405
Clay King, James D. StameyAbstract The idea that medical treatment costs and outcomes might be connected is not new. Likewise, as long as researchers have been designing clinical trials and public opinion polls, there has been interest in the sample size necessary to obtain a desired level of precision and certainty before collecting the data. However, researchers continue to adapt costeffectiveness models for scenarios of

Recognizing distributions using method of potential functions Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210405
Piotr SulewskiAbstract This article focuses on the idea of recognizing distributions rather than performing classic goodnessoffit tests (GoFTs). In order to recognize distributions, the method of potential functions (MoPF) is used, focusing the reader’s attention on recognizing the normal distribution. The prevailing part of the article concentrates on the implementation of a classifier of distributions that involves

Ranked set sampling with lowest order statistics for Pareto distribution Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210402
Dinesh S. Bhoj, Girish ChandraAbstract Ranked set sampling (RSS) is a method of sampling that can be advantageous when quantification of all sampling units is costly but when small sets of units can be ranked according to the character under investigation by means of visual inspection or other methods not requiring actual measurements. RSS performs better than simple random sampling (SRS) to estimate the population mean. In original

Mixture design model with uncertain response and its application Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210323
Weixia Li, Zhibin Zhu, Xiaoyuan Zhu, Chongqi ZhangAbstract In the application of mixture experiments, it’s hard to model the outputs with the form of persons’ description words mathematically. This paper models the outputs by fuzzy numbers and proposes a mixture model for such kind responses. The parameters of the proposed model are estimated by iterative least square method, and the goodness of fit of the model is evaluated by the average closeness

Exact and approximate computation of critical values of the largest root test in high dimension Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210323
Gregory Tai Xiang Ang, Zhidong Bai, Kwok Pui Choi, Yasunori Fujikoshi, Jiang HuAbstract The difficulty to efficiently compute the null distribution of the largest eigenvalue of a MANOVA matrix has hindered the wider applicability of Roy’s Largest Root Test (RLRT) though it was proposed over six decades ago. Recent progress made by Johnstone, Butler and Paige and Chiani has greatly simplified the approximate and exact computation of the critical values of RLRT. When datasets are

Moving average EWMA chart for the Weibull distribution Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210323
Nasrullah Khan, Muhammad S. Nawaz, Rehan A. K. Sherwani, Muhammad AslamAbstract A new control chart is being presented using an exponentially weighted moving average (EWMA) statistic and moving average (MA) statistic to monitor the number of defective counts before specified time which follows Weibull distribution. Steps for construction of the control charts are presented, and the efficiency of the control chart is measured using average run length (ARL). To find ARL

On the construction of mixedlevel rotatable response surface designs when experimental unit experiences overlap effects Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210323
Ankita Verma, Seema Jaggi, Eldho Varghese, Cini Varghese, Arpan Bhowmik, Anindita Datta, Hemavathi MAbstract This paper describes the response surface model for mixedlevel factors of the form 2n×3 where experimental units/plots experience the overlap effects from immediate left and right neighboring units. Conditions have been derived for the orthogonal estimation of the parameters of the model. A method of constructing mixedlevel response surface designs of the form 2n×3 has been proposed. The

Constructing UpSet plot for survey data with weights using SAS and R software Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210323
Camilo Gomez, Alexander V. Goponenko, Julia N. SoulakovaAbstract There is a lack of data visualization tools, such as UpSet plot, suitable for survey data collected via complex sampling. Ignoring the design specifications when plotting the data could result in ambiguous findings. We discuss a twostep approach that can be used to construct the UpSet plots of weighted frequencies and relative weighted frequencies (percentages). In the first step we compute

Testing similarity between firstorder intensities of spatial point processes. A comparative study Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210319
I. FuentesSantos, W. GonzálezManteiga, J. MateuAbstract Testing whether two spatial point processes have the same spatial distribution is an important task that can be addressed from different perspectives. A KolmogorovSmirnov test with asymptotic calibration and a Cramer von Mises type test with bootstrap calibration have recently been developed to compare the firstorder intensity of two observed patterns. Motivated by common practice in epidemiological

Automatic clustering algorithm for interval data based on overlap distance Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210319
Ngoc Lethikim, Tuan Lehoang, Tai VovanAbstract In this study, the improved overlap distance is used as a criterion in order to build clusters for interval data. This distance has shown the suitability, and given an outstanding advantage in evaluating the similarity for intervals with a lot of the considered data sets. Based on the overlap distance, we propose the Automatic Clustering Algorithm for Interval data (ACAI). One of the best

Procedure for the identification of multiple influential observations in block design for incomplete multiresponse experiments in presence of masking Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210318
Raju Kumar, Lal Mohan BharAbstract In agricultural experiments, if outliers are present in a data set, inference of experiment may be reversed. The purpose of this article is to develop a method for detection of subset of outlier vectors in block designs for incomplete multiresponse experiments in presence of masking. We defined an influence matrix comprising of Cookstatistics in its diagonal and product of two Cookstatistics

Pairwise comparisons for Levenestyle variability parameters Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210317
Dennis D. Boos, Kaiyuan Duan, Xiaoni LiuAbstract The simplest way to test for equality of scale in oneway data is to use the analysis of variance applied to absolute deviations from sample medians in place of the original data. This approach started by Levene (1960, using means instead of medians), appears in most statistical packages and is quite powerful for detecting heterogeneity of scale. However, researchers often want to know where

Measuring the symmetry of model errors for varying coefficient regression models based on correlation coefficient Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210316
Yujie Gai, Yusheng Wei, Jun Zhang, Aixian ChenAbstract In this paper, we propose a residuals based estimator of kth correlation coefficient between the density function and distribution function for varying coefficient regression models, and further we use this kth correlation coefficient to test whether the density function of the true model error is symmetric or not. First, we propose a moment based estimator of kth correlation coefficient

Choice between and within the classes of PoissonTweedie and PoissonexponentialTweedie count models Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210316
Rahma Abid, Célestin C. KokonendjiAbstract In both flexible Poisson Tweedie (PT) and PoissonexponentialTweedie (PET) overdispersed count models, the common power parameter p∈{0}∪[1,∞) works as an automatic distribution selection. It mainly separates two subclasses of zeroinflated ( 1≤p<2) and heavytailed (p > 2). Although extensive works have been conducted in discriminating between continuous and semicontinuous distribution functions

An algorithm for computing the tsignature of twostate networks Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210316
Mohammad Siavashi, Somayeh ZarezadehAbstract Due to the importance of the signature vector in studying the reliability of networks, some methods have been proposed by researchers to obtain the signature. The notion of signature is used when at most one link may fail at each time instant. It is more realistic to consider the case where none of the components, one component, or more than one component of the network may be destroyed at

A comparative study on highdimensional bayesian regression with binary predictors Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210316
Debora Slanzi, Valentina Mameli, Philip J. BrownAbstract Bayesian regression models have been widely studied and adopted in the statistical literature. Many studies consider the development of reliable priors to select the relevant variables and derive accurate posterior predictive distributions. Moreover in the context of small highdimensional data, where the number of observations is very small with respect to the number of predictors, sparsity

Testing for measurement error in regression with autoregressive innovations Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210316
Himanshu Pokhriyal, N. BalakrishnaAbstract This paper analyses the effect of measurement errors in the linear regression model with autocorrelated errors using a Lagrange Multiplier (LM) test. The asymptotic distribution of test statistic is shown to be standard normal under the null hypothesis. Finite sample properties of the test are examined briefly by simulations. It is found that the power of the test depends on the variance

Doptimal design for estimation of optimum mixture in a threecomponent mixture experiment with two responses Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210315
Manisha Pal, Nripes Kumar Mandal (Retd.), Hare Krishna MaityAbstract The paper studies a mixture experiment with two responses – a primary response and a secondary response. The primary response function is assumed to be quadratic in the mixing proportions, while the secondary response is a linear function of the proportions. Optimum designs are investigated for estimating the optimum mixture combination that maximizes the mean primary response subject to the

Robust quadratic discriminant analysis using Sn covariance Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210315
O. K. Sajana, T. A. SajeshAbstract This paper presents a robust method for robust estimation of quadratic discriminant analysis. The mean and covariance matrix for estimating quadratic discriminant rule is computed using a robust estimation method called Sn method established from a robust covariance estimator SnCov. The performance of the proposed method is evaluated using the results of simulated samples. This outlier detection

Analysis of temperature and humidity in Oman using singular spectrum analysis Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210314
S. AL Marhoobi, A. PepelyshevAbstract The purpose of this paper is to study hourly time series of temperature and humidity from six meteorological stations in Oman from 2009 to 2018. Since our data contains missing values due to device failures, we have compared several methods of imputation and found that regression with lagging is most reasonable. To extract the annual oscillations and daily periodicities from hourly time series

Robustness of interpretable components in relation to the effect of outliers using measures and circular distances Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210314
A. M. Silva, M. Resende, M. Facco, A. R. de Morais, M. A. CirilloAbstract The Interpretable Components (IC) use restrictions in order to have a better interpretation of the coefficients related to a Principal Component (PC). The efficiency of a (IC) due to a (PC) is made in relation to the angle formed between the Components, it being desirable the lowest value. In this context, an alternative to enrich this validation is the use of measures and circular distances

A class of bootstrap tests on the tail index Commun. Stat. Simul. Comput. (IF 0.651) Pub Date : 20210314
Eunju HwangAbstract This work proposes powerful tests using bootstrap methods for the tail index in the family of distribution functions with nondegenerate right tail. It is of interest to test whether the right tail of a distribution function is the same as or heavier than that of the Pareto distribution with index m0 for some m0, vs. alternatively the right tail is lighter. Based on the nonparametric tests