Abstract The ridge-type estimators have been intensively studied and modified for the linear regression model. In this article, we introduce a modified unbiased two-parameter 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 two-parameter estimator (UTPE). We give a simulation

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract Multiple category prevalence represents the prevalence of the specific disease with different k-category (k > 2) statuses, such as mild, moderate and severe. This study proposed the Bayesian method for the meta-analysis of studies with multiple category prevalence. The Dirichlet-multinomial model was used to obtain the Bayesian approach. In this way, both the opportunity to consider the preinformation

Abstract We consider the problem of logarithmic interval estimation for a cross-product 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

Abstract 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 non-sampling

Abstract In this paper, asymptotic normal, approximate F-based and BCa bootstrap inferences are proposed for the stress-strength reliability model P(X

Abstract This article presents a statistic to identify the proportional intensity of an area to belong to a space-time cluster, which is very important in spatial statistics. A significant extension of the so-called F-function 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

Abstract This paper proposes new non-model 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

Abstract Based on record statistics from three-parameter modified Weibull distribution, we consider the problem of estimating the unknown parameters using Bayesian and non-Bayesian approaches. Under a continuous-discrete 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

Abstract 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

Abstract Multiple imputation is a well-established 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

Abstract Fisher’s exact test and Pearson’s chi-squared test are frequently used for testing associations of two binary variables in 2-by-2 contingency tables. In the single test setting, many studies have shown that the asymptotic Pearson’s chi-squared test cannot preserve the test size for small samples and Fisher Exact test tends to be overly conservative. Multiple unconditional exact tests were

Abstract In this paper, stress-strength model is studied for an inverted exponential Rayleigh distribution (IERD) when the latent failure times are progressively Type-II censored. When both strength and stress random variables follow common IERD scale parameters, the maximum likelihood estimate of stress-strength reliability (SSR) is established and the associated approximate confidence interval is

Abstract 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 objective-tailored multiple testing procedure based on the covering

Abstract 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 2-norm regularization term. The regularization parameter that controls the equilibrium

Abstract 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)

Abstract In this article, we provide a consistent method of estimation for the parameters of a three-parameter 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

Abstract 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

Abstract In this paper we consider multi-server single queue system in which inter-arrival 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract This paper addresses the coherent forecasting problem for overdispersed integer-valued 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

Abstract 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

Abstract 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 thick-tailed. In order to model skewed and thick-tailed disturbances, we specify a highly flexible Generalized Tukey Lambda (GTL) distribution that can closely mimic many other unimodal distributions. The GTL-based maximum

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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,

Abstract 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

Abstract 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 length-biased sampling. Some simulation studies are conducted to indicate ME confidence intervals have better coverage probabilities

Abstract 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

Abstract 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 cost-effectiveness models for scenarios of

Abstract This article focuses on the idea of recognizing distributions rather than performing classic goodness-of-fit 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract This paper describes the response surface model for mixed-level 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 mixed-level response surface designs of the form 2n×3 has been proposed. The

Abstract 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 two-step approach that can be used to construct the UpSet plots of weighted frequencies and relative weighted frequencies (percentages). In the first step we compute

Abstract Testing whether two spatial point processes have the same spatial distribution is an important task that can be addressed from different perspectives. A Kolmogorov-Smirnov test with asymptotic calibration and a Cramer von Mises type test with bootstrap calibration have recently been developed to compare the first-order intensity of two observed patterns. Motivated by common practice in epidemiological

Abstract 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

Abstract 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 Cook-statistics in its diagonal and product of two Cook-statistics

Abstract The simplest way to test for equality of scale in one-way 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

Abstract In this paper, we propose a residuals based estimator of k-th correlation coefficient between the density function and distribution function for varying coefficient regression models, and further we use this k-th 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 k-th correlation coefficient

Abstract In both flexible Poisson Tweedie (PT) and Poisson-exponential-Tweedie (PET) over-dispersed count models, the common power parameter p∈{0}∪[1,∞) works as an automatic distribution selection. It mainly separates two subclasses of zero-inflated ( 1≤p<2) and heavy-tailed (p > 2). Although extensive works have been conducted in discriminating between continuous and semicontinuous distribution functions

Abstract 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

Abstract 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 high-dimensional data, where the number of observations is very small with respect to the number of predictors, sparsity

Abstract This paper analyses the effect of measurement errors in the linear regression model with auto-correlated 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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