Abstract We develop the quadratic inference function method to the parameter estimation of the marginal models with general relative risk form and common baseline hazard function for multivariate failure time data. The usual exponential relative risk form is relaxed to an non-negative twice differentiable form. The proposed estimator, under some regularity conditions, is shown to be consistent and

Abstract Estimating the hazard rate function, which is one of the most important ways for representing the lifetime distribution in the survival analysis, has been studied by many researchers. A problem arises when estimating the hazard rate function near the boundary points. This problem is called the boundary bias problem. To solve this problem a variety of techniques have been developed in the literature

Abstract The inverse moment’s research can be used in many fields such as risk evaluation and insurance mathematics. In this paper, we consider the weighted inverse moment model based on asymptotically almost negatively associated (AANA, in short) sequences. Under some weak condition, we obtain the asymptotic approximation of inverse moments and its convergence rate. As applications of inverse moments

Abstract Generalized linear models (GLM) applications have become very popular in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in these models. In this paper, we introduce new first-order approximated (FOA) estimators in the case of gamma distributed response variables in GLMs. Also, the generalization of

Abstract In this article, we propose five prediction intervals for the beta autoregressive moving average model. This model is suitable for modeling and forecasting variables that assume values in the interval (0, 1). Two of the proposed prediction intervals are based on approximations considering the normal distribution and the quantile function of the beta distribution. We also consider bootstrap-based

Abstract The discrete Pareto (DP) distribution studied in this paper is a probability model with a power-law tail, which provides a convenient alternative to the well-known Zipf distribution. While basic characteristics of the DP model are available explicitly, this is not an exponential family and parameter estimation connected with this model is a challenging task. With this in mind we develop a

Abstract In this article we study the semiparametric estimation of the Tobit model simultaneously jointed with an endogenous regressor. The model usually is estimated by maximum likelihood method and two-stage estimation under the normality assumption of the error terms. The possible misspecification motivates our semiparametric study. We first construct a semiparametric estimator for the parameters

Abstract In otolaryngologic studies, subjects may contribute either unilateral observations from only one ear or bilateral observations from a pair of ears. For bilateral subjects, each person contributes information from two ears, the values of which are generally highly correlated. To avoid the confounding effect in stratified otolaryngologic studies, stratified data analysis is an important research

Abstract Quantile functions of the response variable provide a tool for practitioners to analyze both the central tendency and statistical dispersion of data. As a counterpart to the regression tree models, quantile regression tree methods (QRT) gained interest in constructing tree models for quantile functions. Previous QRT methods, however, estimate different tree models for each quantile level as

Abstract This paper proposes a flexible stochastic volatility model with jumps and leverage effect by introducing a generalized hyperbolic skew Student’s t-distribution (GHST), which can capture the skewness, heavy-tailedness and leptokurtosis of asset returns. Monte Carlo Markov Chain algorithm combining Gibbs sampler and Metropolis-Hastings algorithm is used for parameter estimation and the method

Abstract The common used goodeness-of-fit tests are based on the empirical distributions functions (EDF) where distances between empirical and theoretical hypothesized distributions are compared to critical values. The aim of this paper is to provide for different sample sizes, tables of goodness-of-fit critical values of modified Kolmogorov-Smirnov statistic Dn, Anderson-Darling statistic A2, Cramer-Von

Abstract We suggest a new class of difference-type estimators under two-phase stratified sampling when using information from two auxiliary variables including their distribution functions. The bias and MSE expressions are derived up to first degree of approximations. It is observed that the proposed class of difference-type estimators is more efficient as compared to the estimators: sample mean, ratio

Abstract Zero inflated distributions are used in a wide variety of phenomena, particularly when the response variable exhibits an excessive number of zeros. These phenomena can lead to an overdispersion in the data and the reduction of its mean. In this paper, we aim to address this issue by introducing the zero inflated Waring distribution (ZIW). The latter is regarded to fit the data more adequately

Abstract The goal of network analysis is to focus on relationships between social entities. It is used widely in the social and behavioral sciences, as well as in political science, economics, psychology andorganizational science. The social network approach has a long history and it has been developed over the last 60 years by researchers in psychology, sociology and anthropology. Nowadays, using

Abstract Estimation of the population variance is of major concern in numerous examinations and ratio estimators are mainstream choices for it. It is a typical practice to utilize conventional central moments to construct variance estimators utilizing auxiliary information. However, conventional central moments give equal weight to each observation and hence highly affected by extreme values. For solving

Abstract The assumption of normality has a crucial importance in many statistical procedures. Therefore, a number of normality tests are proposed. Also, many investigations are conducted on the performance of these normality tests under a set of alternative distribution. However, there are few studies to compare the performance of the commonly used normality tests in the presence of outliers, but they

Abstract Adapted from the acceptance sampling field, the double sampling monitoring schemes implement a two-stage strategy to decide whether the process being monitored is in-control or out-of-control. That is, a master sample is split into two separate subgroup samples, with the first subgroup sample used in the first stage and, depending on which type of double sampling method is used, either only

Abstract In this study, we propose a new gray model called “normalized beta function gray model”. Its main idea is to use the normalized beta function to optimize the linear gray action quantity into a nonlinear form. It is worth mentioning that when solving the nonlinear parameters of the newly proposed model, we construct a nonlinear optimization problem with constraints by taking the mean square

Abstract In this article, we consider the estimation problem of Unit Gompertz distribution with parameters α and β under the framework of dual generalized order statistics. This article is purely devoted to present the Bayesian view of estimation of Unit Gompertz distribution. For this purpose, we consider two widely popular approximation methods called Markov chain Monte Carlo and Lindley approximation

Abstract Minimal balanced and strongly balanced repeated measurements designs are preferred to balance out residual effects which are major source of bias. In the situations where these minimal balanced designs could not be constructed, minimal weakly balanced are preferred. In this article, therefore, some new generators are developed to obtain the classes of minimal circular weakly balanced repeated

Abstract In lifetime experiments, sometimes censoring is inevitable to save experimental cost and to accelerate execution of the test. For conducting a comparative lifetime experiment of the products, which are from different production lines under the same environmental conditions, the joint type-II censoring scheme is practically much significant. The present article deals with inferences, when jointly

Abstract The gray GM (1 N) model is an important type of prediction model. However, the conventional model’s prediction method has the following two defects: first, the model’s parameter estimate is the approximate value; second, the model’s time response equation has the approximation solution. Because the gray GM (1, N) model involves the multivariable time sequence, the variables form a whole through

Abstract This study aims to determine the most appropriate warranty period and policy with the lowest warranty cost from the manufacturer/seller point of view. The warranty policy to be put forward and the warranty period to be offered under this policy should ensure customer satisfaction while at the same time causing a minimum cost to the manufacturer/seller. For this reason, the optimum warranty

Abstract In this article, we propose the median-of-means type nonparametric estimator for S-Gini index by using the idea of grouping under the massive data framework, which has been widely used in economics, finance, and insurance. Under certain condition on the growing rate of the number of subgroups, the consistency and asymptotic normality of proposed estimator are investigated. Furthermore, we

Abstract The least-squares (LS) is a known estimator for the estimation of the linear regression model parameter. The LS is inefficient in the happening of the significant correlations among the explanatory variables. Alternatively, we propose a new regression estimator for the purpose of the reduction of multicollinearity effects. The comparisons between the new regression estimator with each of the

Abstract We propose two Bayesian methods for regularized left censored regression: the reciprocal Bayesian bridge and the reciprocal Bayesian adaptive bridge. Gibbs samplers are derived based on the reciprocal Bayesian bridge prior which can be written as a scale mixture of inverse uniform distribution. The proposed approaches are then illustrated via five simulated studies and a real data example

Abstract Based on a new lifetime test scheme proposed in this article called double Type-I hybrid censoring scheme, the point estimates and confidence intervals of the unknown parameter, reliability function and failure rate function of the Rayleigh distributed lifetime are obtained. According to the posterior predictive distribution, the predictive estimates are given under three loss functions. Monte-Carlo

Abstract An improved class of imputation procedures are proposed for estimating the finite population mean by using the known population mean and higher order moments of the auxiliary variable. Mathematical expressions for bias and mean square error of proposed and existing imputation procedures are derived up to first order approximation. The theoretical results are supported by numerical study of

Abstract In this article, we propose two new scale invariant test statistics when the available data are subject to progressively Type-II censoring. The proposed tests are based on Gini index of spacings. It is observed thorough extensive Monte Carlo simulations that the proposed tests are quite powerful in compare to similar existing goodness-of-fit tests studied by Balakrishnan et al. and Wang. We

Abstract The Almon technique is a widely used estimation procedure for the distributed lag model (DLM) to encounter the problems associated with direct application of the ordinary least squares method to this model. The Almon estimator may be sensitive to outliers in the y-direction. This study is aimed to propose a robust estimator for parameter vector of the DLM when data set is contaminated with

Abstract We propose a semiparametric regression defined from the generalized odd log-logistic Maxwell distribution under the cubic spline with linear and nonlinear effects for censored and uncensored data. The parameter estimates for this regression are determined using penalized likelihood. Global influence diagnostics and quantile residuals are addressed in new standards. Several Monte Carlo simulations

Abstract The exponential model is the simplest among all lifetime distribution models and it possesses a constant failure rate. Here we propose a new class of lifetime distribution having decreasing failure rate which we developed through compounding exponential distribution with the positive hyper-Poisson distribution. We investigate some of its statistical properties and employed various methods

Abstract This paper is concerned with the simulation of an extension of the Mallows-Bradley-Terry ranking probability model by the acceptance-rejection method. A Monte Carlo Markov Chain (MCMC) algorithm for the simulation of the model has been already proposed when the number q of items to be ranked is large, say more than 7. However, in most real life situations the number q of items to be ranked

Abstract In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes

Abstract We consider two classes of exponential dispersion models of discrete probability distributions which are defined by specifying their variance functions in their mean value parameterization. These classes were considered in our earlier paper as models of overdispersed zero-inflated distributions. In this paper we analyze the application of these classes to fit count data having overdispersed

Abstract The problem of classification into two normal populations with a common mean and ordered variances is revisited. So far in the literature, authors have proposed classification rules which are based on either the Graybill-Deal estimator or its improved version of the common mean under order restricted variances. Surprisingly, the maximum likelihood estimator (MLE) of the common mean has not

Abstract A bivariate generalized Yule distribution is introduced here as the distribution of the random sum of certain types of independent and identically distributed bivariate Bernoulli random variables. We study some important properties of the distribution through deriving explicit expressions for its probability mass function, factorial moments, and the probability generating function (p.g.f.)

Abstract Simultaneously monitoring of process mean and dispersion for the normal process has gained considerable attention. In this manuscript, we have proposed a maximum exponentially weighted moving average (Max-EWMA) control chart for joint monitoring of mean and dispersion using the inverse response function when the process follows Weibull distribution. The average run length (ARL) and the standard

Abstract Several methods to perform an ANOVA with a binary dependent variable in 2-way layouts are compared with the parametric F-test. Equal and unequal cell counts as well as several different effect models are taken into account. Special attention has been paid to heterogeneous conditions, which are caused by nonnull effects through the relation of the binomial probability and its variance. For

Abstract This article extends spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) which is the most popular spatial econometric model to the case of an arbitrary finite number of nonparametric additive terms and spatial autoregressive models with spatial autoregressive disturbances of arbitrary finite order (SARAR(R,S)). We propose a sieve two-stage least squares (S2SLS)

Abstract The purpose of this study is to investigate the effects of temporal aggregation on modeling and testing a single variance-change in a univariate time series process. We show that the aggregation effect on the variance-change ARMA model can be explained only with non-aggregate model parameters, based on functional links between a non-aggregate process and its m-aggregate process. In addition

Abstract The two-sample Kolmogorov–Smirnov and Anderson–Darling tests assess the hypothesis that two given samples come from the same population. In this paper, the power of each test was measured using a variety of alternative distributions and varying the sample size. Recommendations for the more powerful test for each common distribution are given, depending on whether the distribution has heavy

Abstract In this paper, we address the estimation of fixed effects parameters in the zero-inflated negative binomial mixed model based on shrinkage estimators, namely linear shrinkage, pretest, shrinkage pretest, shrinkage, and positive-shrinkage estimators when the random effects are considered as nuisance parameters. We compare the performance of the shrinkage estimators to unrestricted and restricted

Abstract A commonly used test for the independence of two sets of variables from a normal multivariate population is the Wilks Lambda test or the multivariate likelihood ratio test (LRT). However, this test’s performance is highly influenced by outliers and non-normality of the data and thus, robust test statistics should be used, allied to Monte-Carlo methods. In this paper, we proposed and evaluated

Abstract The Fay–Herriot (FH) model is widely used in small area estimation (SAE) for aggregated level data, but in several applications presence of spatial effect between contiguous or neighboring region cannot be denied which is not handled by this model. Conditional Autoregressive and Simultaneous Autoregressive specifications do incorporate spatial associationship while taking into account the

Abstract Resampling-based confidence intervals based on medians are examined for pairwise comparison of scale. Methods that have been found in the literature to be effective for comparing scale for two groups are extended to the case of all pairwise comparisons, using a Tukey-type adjustment to ensure simultaneous confidence. Coverage rate and interval width estimates were computed using simulated

Abstract Using control charts to monitor healthcare systems has gained particular attention. In this paper, a risk-adjusted cumulative sum control chart is designed to monitor surgery outputs. Before undergoing surgery, the patients have some unique risk factors which influence the surgery outputs. Thus, risk-adjustment is carried out with the purpose of taking these risks into account using an accelerated

Abstract Forecasting risk measures in order to minimize and prevent a worse risk is an important and challenging task in quantitative risk management. Methodology and assessment of forecast accuracy are still developed to give a better risk measure forecast. In this paper, we provide a simple procedure to forecast expected-based risk measures of Value-at-Risk (VaR) and Expected Shortfall (ES). These

Abstract In this article, we have concentrated on the influence diagnostics based on the mean shift outlier models (MSOM) and case deletion methods in the linear mixed measurement error models with Ridge estimation using the corrected likelihood of Nakamura (Biometrika 77 (1):127–37). We have derived the corrected score test statistic for outliers detection according to MSOM. Then, several case deletion

Abstract Let Poisson variables X1,⋯,Xm be independent and Y1,⋯,Yn be also independent such that each Xi∼P(λi),Yj∼P(μj). We prove that if Xm:m=max{X1,⋯,Xm} and Yn:n=max{Y1,⋯,Yn} have the same distribution, then m = n and {λ1,⋯,λm} = {μ1,⋯,μm}. The weak version in term of minimum statistics is also derived.

Abstract Multi-way arrays or tensors are becoming important tools to deal with multidimensional data. This paper is concerned with a one-class classification of second order tensor data. One-class classification problems arise when data from one class is the only data available. Typical vector based methods have limitations when dealing with tensor data. Least squares one-class support vector machine

Abstract We consider the problem of nonparametric estimation of certain conditional models when the explanatory variable is functional. The models studied are the Conditional Expectation (CE), the Conditional Distribution Function (CDF) and the Conditional Probability Density (CPD). The estimators are constructed by combining the local linear method and the k-Nearest Neighbors (kNN) smoothing approach

Abstract In this article we propose a new online control system aiming to lower the instants in which the production process migrates from being in- to out-of-control state, which generates an increase in the non-conformity rates. As shifts from in- to out-of-control are non-deterministic, a sample of size n is collected, for each m or L units produced, and each element from the sample is imprecisely

Abstract The objective of this paper is to propose an efficient regression algorithm of survival analysis - SurvivalBoost.. This algorithm is based on Random Survival Forests (RSF) and XGBoost. By combining the Elastic-Net penalty type Cox proportional hazards regression model with XGBoost optimal algorithm, our algorithm is more suitable for survival analysis. The performance of the proposed algorithm

Abstract We compared the robust methods in the general linear regression (GLR) model through simulations. The results show that the S-type estimators produce the minimum mean squared error (MSE) of the model in all samples and the minimum standard errors of the estimators of the regression coefficients in almost all samples in all situations including the normal distribution despite their modesty in

Abstract Recently, researchers have shown an increased interest in combining Statistical Process Control/Monitoring and Social Network Analysis. One approach to detect anomalies in social networks is to monitor some summary statistics of the network structure using control charts. We aim in this study to conduct a performance comparison among some network centrality measures (betweenness, closeness

Abstract This article considers the efficient and robust estimators for linear regression models. In this article, we develop an adaptive efficient and double-robust estimator based on generalized empirical likelihood framework and weighted least squares. Efficiency is ensured through minimizing the discrepancy statistics and the double robustness is obtained via weighted least-square as well as downweighting

Abstract We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence component, then conduct group variable selection. The group bridge penalty is adopted for bi-level

Abstract In this paper, we analyze an M/PH/1 queuing system with optional re-service. In this queueing system the interarrival time distribution is exponential with mean 1/λ, service time and re-service time distribution follows continuous time phase-type distribution and each customer will be re-serviced with probability ‘p’. We have obtained the expressions of various system performance measures

Abstract In order to incorporate group information for different groups, we extend the partial envelopes (Su and Cook, Statistica Sinica 23:213–30, 2011) to the groupwise partial envelopes which can improve the efficiency of parameter estimation and enlarge the scope of partial envelope model. It maintains the potential of the original partial envelope methods to increase efficiency and allows for