Abstract A large amount of literature has been developed for the presence of multicollinearity among the explanatory variables that are often performed with the aim of reducing the undesirable effects on the maximum likelihood estimate(MLE). In particular, many authors have discussed ridge estimation (RE) under the framework of the mean regression model, because the RE enjoys the advantage that its

Abstract In this paper, we introduce a first-order integer-valued autoregressive process with zero-modified Poisson-Lindley distributed innovations based on the binomial thinning operator. Some statistical and conditional properties of the model are presented, several estimation methods are applied to estimate the unknown parameters and, for the Yule-Walker and conditional least square estimators,

Abstract The concordance correlation coefficient (CCC) is a common tool to assess agreement among multiple observers for continuous and discrete responses. However, previous results in the statistical literature have shown that the CCC estimators may suffer from a bias problem under a misspecified model for normal data. In order to avoid fitting data with a misspecified model, thus yielding biased

Abstract Time between events (TBE) control charts have been widely used to monitor high yield processes. Traditionally, an estimated in-control occurrence rate from a Phase I dataset is used to calculate the control limits when the rate is unknown. However, when Phase I analysis is time consuming or costly, the traditional Phase I/Phase II approach is not feasible. A self-starting method that sequentially

Abstract Control charts based on Time Between Events (TBE) have been shown to be effective in high quality processes, where the defects or failures (events) occur at a very low probability. The occurrences of events are usually modeled as a homogeneous Poisson process. The TBE data is likely to follow a skewed distribution, such as the gamma distribution. In order to monitor high quality processes

Abstract In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed a hybrid non-stationary model with Elman’s Recurrent Neural Networks (ERNN). The proposed model is non-stationary in trend component with lagged variable, average of all past observations and ERNN. This model can capture both linear and non-linear

Abstract This paper presents the conditional least squares (CLS) estimation procedure for the class of first-order spatial integer-valued autoregressive processes, denote by SINAR(1, 1). We derive the asymptotic properties of the CLS estimators of model parameters. Simulation results are presented to assess the performance of estimators under finite sample sizes and under equidispersion and overdispersion

Abstract This paper examines price bubbles in the relatively new carbon emission trading scheme of Beijing carbon market by employing a recently proposed econometric test which can stamp the occurrence and burst of financial bubbles. We find multiple bubbles in Beijing carbon market over the sample period between January 2014 to April 2018, and that the occurrences of carbon price bubbles are closely

Abstract In this paper, some results on the complete convergence for randomly weighted sums of widely orthant-dependent (WOD, for short) random variables are established under some general conditions, extending the corresponding ones obtained in the literature. By using the results that we presented, we further investigate the convergence of the state observers of linear-time-invariant systems based

Abstract Longitudinal clustering provides a detailed yet comprehensible description of time profiles among subjects. With several approaches that are commonly used for this purpose, it remains unclear under which conditions a method is preferred over another method. We investigated the performance of five methods using Monte Carlo simulations on synthetic datasets, representing various scenarios involving

Abstract One of information needed in weather forecast is air temperature. This value might change any time. Prediction of air temperature is very valuable for some communities and occasions. Therefore, high accuracy prediction is needed. Since the information about air temperature might vary over time, it is necessary to implement methods that can adapt to this situation. The use of neural network

Abstract The generalized estimating equations (GEE) procedure is a common approach for regression analyses of correlated responses, which is common for data obtained in longitudinal studies. Selecting a working correlation structure has been a momentous topic in longitudinal data analysis using GEE. Although consistent and asymptotically normal estimates can be obtained with misspecified working correlation

Abstract Generalized Estimating Equations (GEE) is used to analyze repeated measurements taken from subjects at equal time intervals and is applicable in presence of missing data. In this study, we aimed to introduce Quasi-Least Squares Regression (QLS), which is an extension of GEE and applicable when time intervals are unequal, and compare model performances under different scenarios in terms of

Abstract We derive new approximations for the Value at Risk and the Expected Shortfall at high levels of loss distributions with positive skewness and excess kurtosis, and we describe their precisions for notable ones such as for exponential, Pareto type I, lognormal and compound (Poisson) distributions. Our approximations are motivated by that kind of extensions of the so-called Normal Power Approximation

Abstract In social sciences, a plethora of studies utilize nominal data to establish the relationship between the variables. This, in turn, requires the correct use of correlation technique. The choice of correlation technique depends upon the underlying assumptions and power of the test of significance. The objective of the research is to explore the best measure of association for nominal data in

Abstract We study the parameter estimation for ergodic diffusion process Xt from perturbed observations Y t j = X t j + ε t j , j = 1 , … , n where t 1 , … , t n are observation times and the noise ε t is a strongly mixing stationary noisy process with the density function g. We construct an estimator of the diffusion parameters based on the minimum Hellinger distance between the density of the invariant

Abstract Several methods have been proposed for detecting zero modification in the first-order integer-valued autoregressive (INAR(1)) process. A basic problem of these tests is that they rely upon asymptotic results. In this paper, an alternative test is introduced which makes direct use of the approximate distribution of the number of zeros, which can be described by a beta-binomial distribution

Abstract This paper presents a novel estimation approach for cumulative link models, based on median bias reduction. The median bias reduced estimator is obtained as solution of an estimating equation based on an adjustment of the score. It allows to obtain higher-order median centering of maximum likelihood estimates without requiring their finiteness. The estimator is equivariant under componentwise

Abstract Out of the many different types of symmetries of a continuous bivariate joint probability density function, focus here is on the central symmetry and symmetry about an axis. Tests for both types of symmetries are proposed and evaluated in the literature. However interestingly, investigation into tests for and a general discussion of either central symmetry or symmetry about an axis don’t seem

Abstract Quantile regression is a very important tool to explore the relationship between the response variable and its covariates. Motivated by mean regression with LASSO for compositional covariates proposed by Lin et al. (Biometrika 101 (4):785–97, 2014), we consider quantile regression with no-penalty and penalty function. We develop the computational algorithms based on linear programming. Numerical

Abstract Gibbs samplings is a Markov Chain Monte Carlo technique for estimating conditional probabilities in Bayesian networks. A major problem of Gibbs sampling is the dependency of the generated chain of samples. Thus the estimates are biased unless the initial value of the chain is drawn from the target distribution. One elegant method to overcome the initial bias is regenerative samplings. We reported

Abstract Score Predictor Factor Analysis (SPFA) was introduced as a method to compute factor score predictors that are – under some conditions – more highly correlated with the common factors resulting from factor analysis than the factor score predictors computed from the factor model. In the present study, we investigate SPFA as a model in its own rights. In order to provide a basis for this, the

Abstract The histogram and boxplot are effective and simple graphical tools, which are broadly used to explore the characteristics of the distribution of univariate data. In this proposed work, two statistical plots called sample variance plots (Sv-plots), are defined which illustrate squared deviations from the sample variance. Sv-plots exhibit the contribution of each data value toward the sample

Abstract In this paper, we price the vulnerable option under the assumption that both underlying and counterparty asset price follow correlated jump diffusion processes. By the two-dimensional Itô-Doeblin formula, we initially derive a partial integro-differential equation (PIDE) satisfied by the price of the vulnerable option with flexible jumps. Then, the double Mellin transform converts the PIDE

Abstract In this paper, a decision-theoretic approach is used to obtain the Bayesian sampling plan (BSP) for the generalized Type-II hybrid censoring scheme when lifetimes of sampled units follow a one-parameter exponential distribution. An efficient loss function is used to decide whether to accept or reject the batch. The BSP is obtained by constructing the closed form of the Bayes decision function

Abstract Repeated measurements designs (RMDs) are most important and useful designs in research of various fields due to their economical property but residual or carry over effects are integral parts of these designs. To cope with these problems, balanced or strongly balanced RMDs should be used. In this manuscript Generators are developed to obtain balanced, strongly balanced and weakly balanced

Abstract Aiming at minimizing the ratio of error with respect to the response variable, the least squares ratio is a relatively new method for estimating the regression parameters. In the current article, the performance of this new approach is compared with the traditional OLS approach in the case when homoscedasticity of errors assumption is violated. A comparison is made through a simulation study

Abstract In this study, the test statistics for one-sided and two-sided multivariate hypotheses to the high-dimensional two-sample problem with one unknown covariance matrix were proposed. The tests were developed based on the idea of keeping as much information as possible from the pooled sample covariance matrix by arranging the blocks along its diagonal. The asymptotic distributions of the test

Abstract In this article, we propose a flexible weighted test of independence based on Cramér-von Mises statistic of the Bernstein empirical copula process. We investigate the asymptotic properties of the new weighted test. A Monte Carlo study is conducted to measure the power performance of the new test under various dependence structures and also to compare the performance with some other alternative

Abstract Efficient estimation for a stochastic volatility (SV) model has been actively pursued in recent years. In this paper, a new Markov chain Monte Carlo (MCMC) algorithm based on a combination of Kalman filtering and the auxiliary sufficiency interweaving strategy (ASIS) is studied. Compared to other MCMC strategies like Stan algorithm (“Rstan”) and the Gibbs algorithm (“R2Winbugs”), it is shown

Abstract We investigate point estimation and confidence interval estimation for the heavy-tailed generalized Pareto distribution (GPD) based on the upper record values. When the shape parameter is known, the bias-corrected moments estimators and maximum likelihood estimators (MLE) for the location and scale parameters are derived. However, in practice, the shape parameter is typically unknown. We propose

Abstract This paper describes an empirical Bayesian estimator of multiple Poisson means based on a novel concept. The idea is to assume two different priors; Jeffreys’ prior for determining the hyperparameter and the uniform prior for the canonical parameter for estimating multiple Poisson means. The validity of this idea is discussed in detail. Furthermore, the empirical Bayesian estimator is constructed

Abstract This article explores the parameter space of multivariate skew normal families having identical distributed marginal distributions under a few autoregressive-moving average correlation structures ( Ω ¯ ): namely, MA(1), MA(2), and AR(2) correlation structures. Such an undertaking escapes triviality since the efficacious { Ω ¯ , δ } parametrization, in terms of analysis of marginal distributions

Abstract Confidence intervals for the population variance in the presence of outliers or spikes in the distribution of a variable of interest are topics that have not been investigated in depth previously. Results derived from a first Monte Carlo simulation study reveal the limitations of the customary confidence interval for the population variance when the underlying assumptions are violated, and

Abstract In the present paper, a general class of estimators has been proposed for estimating the population mean of the study variable using auxiliary variable when the population mean of the auxiliary variable is known in stratified random sampling. The bias and mean squared error of the proposed class of estimators are derived under stratified sampling to the first degree of approximation. Members

Abstract This article focuses on the two sample test of the survival function of the failure time under the current status data and introduces three types of the non-parametric two sample test of the survival function. This paper constructs the empirical likelihood ratio test with a weighted approach for the two sample comparison based on MLE, SMLE, and MSLE. A bootstrap method is given for constructing

Abstract In recent years, the doubly geometric process is started to use as a model to fit the data from a series of events since it provides a more flexible model for wider application than the geometric process. In the applications of the doubly geometric process, the estimation problem associated with the process can arise. In this study, we deal with this problem for the stochastic process model

Abstract This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a multiplicative fashion by an observed confounding variable. We propose a new identifiability condition by using the absolute logarithmic calibration to obtain calibrated variables and the direct-plug-in estimator for the correlation coefficient

Abstract In this article, we propose a generalized class of exponential type estimators for estimating the mean of delicate variable in the existence of non-sensitive two auxiliary variables. The performance of mean estimator is obtained by using generalized two stage optional randomized response model. The expression for the bias and the mean square error are obtained up to first degree of approximation

Abstract This paper addresses the issue of estimating the population variance of a study character in the joint presence of random non-response and measurement errors and its application for estimating variations in biological data. Additional information on two highly positively correlated auxiliary variables has been incorporated to develop a general class of estimators under stratified two-phase

Abstract This article examines the zero numerator problem. This problem occurs when the misclassification rates are so low that both forms of misclassification may not both be present in the validation data. For this problem, the article compares two Bayesian methods for analyzing misclassified binary data that has a validation sub-study and three forms of a non-informative prior distribution. It shows

Abstract This paper studies the empirical likelihood method for a multivariate first-order random coefficient autoregressive (RCAR(1)) model. Based on the modified least square score equation, empirical likelihood method is used to test the randomness of coefficients and estimate the unknown parameters. First, the limiting distribution of the log empirical likelihood ratio statistic is established

Abstract By using the Marcinkiewicz-Zygmund type inequality and Rosenthal type inequality, we study the Lr convergence, weak law of large numbers and the complete convergence for usual normed sums and weighted sums of arrays of rowwise widely orthant dependent (WOD, for short) random variables. In addition, some applications of the Lr convergence and the complete convergence to nonparametric regression

Abstract The multiple indicator cluster survey (MICS) is a household survey tool designed to obtain internationally comparable, statistically rigorous data of standardized indicators related to the health situation of children and women. Missing data in a large number of categorical variables are a serious concern for MICS, following complex dependency structures and inconsistency problems that impose

Abstract Clustering the genes is a step in microarray studies which demands several considerations. First, the expression levels can be collected as time-series which should be accounted for appropriately. Furthermore, genes may behave differently in different biological replicates due to their genetic backgrounds. Highlighting such genes may deepen the study; however, it introduces further complexities

Abstract Predicting time-to-death for patients is one of the most important issues in survival analysis. A dynamic prediction method using a bivariate failure time model allows one to build a prediction formula based on tumor progression status observed during the follow-up. However, the existing spline models for the baseline hazard functions are not convenient for predicting long-term survival probability

Abstract Joint modeling technique is a recent advancement in effectively analyzing the longitudinal history of patients with the occurrence of an event of interest attached to it. This procedure is successfully implemented in biomarker studies to examine parents with the occurrence of tumor. One of the typical problem that influences the necessary inference is the presence of missing values in the

Abstract The randomness of the biases of parameter estimates, residuals and some variables is seldom considered for calculating covariance matrix of parameter estimates in total least squares (TLS) iterative algorithm. There are few studies on the precision estimation with the approximate probability distribution method of function in the TLS. In order to get more reasonable precision information for

Abstract In this paper, a new two-type parameter estimator is proposed. This estimator is a generalization of the new two parameter (NTP) estimator introduced by Yang and Chang, which includes the ordinary least squares (OLS), the generalized ridge (GR) and the generalized Liu (GL) estimators, as special cases. Here, the performance of this new estimator is, theoretically, investigated over the OLS

Abstract The binomial exponential distribution as well as its generalizations power binomial exponential and generalized binomial exponential have been introduced in the statistical literature as more flexible models than other probability distributions with support on the positive real line. Different algorithms have been proposed to generate by computer random samples from these new distributions

Abstract In biomedical or clinical studies, semi-competing risks data are often encountered in which one type of event may censor an other event, but not vice versa. An illness-death model is proposed to analyze these semi-competing risks data in the presence of interval censoring on both intermediate and terminal events. The Cox proportional hazards model is employed with a frailty effect to incorporate

Abstract Two fractional factorial designs are called isomorphic if one can be obtained from the other by relabeling the factors, reordering the runs and switching the levels of factors. Given a set of all orthogonal designs (ODs) with n runs, q levels and s factors, it may have several non-isomorphic subclasses. Once a new OD with this design size is generated, it is interesting to know which subclass

Abstract Detecting the abnormal degradation rate of Wiener processes is important for quality control and reliability management. In this paper, we propose upper-side CUSUM control schemes for monitoring the abnormal degradation rate of three classes of Wiener processes. Average run length and standard deviation run length are applied to evaluating the efficiency of CUSUM control schemes. The results

Abstract In this paper, estimation of parameters α and β for the log-extended exponential-geometric distribution will be respectively considered in cases when β is known and when both are unknown. Simple random sampling (SRS) and moving extremes ranked set sampling (MERSS) will be used, and several traditional estimators will be considered. The estimators using MERSS are compared to the corresponding

Abstract In this work, the residual extropy for measuring uncertainty involved in the remaining lifetime of a component has been investigated based on records. Some intriguing properties based on residual extropy of records have been discussed. The study also proposes a simple estimator for residual extropy of records and the performance of the proposed estimator has been evaluated as well. A detailed

Abstract In recent years, application of feature selection methods in biological datasets has greatly increased. By using feature selection techniques, a subset of relevant informative features is obtained which results in more interpretable model improving the prediction accuracy. In addition, ensemble learning can further provide a more robust model by combining the results of multiple statistical

Abstract We propose and investigate a new test of model instability in the mean. The test is based on p-variation of stepwise CUSUM process. We establish a limiting distribution of the test statistics under null as well as under contiguous alternative.

Abstract Community detection is an effective exploration technique for analyzing networks. Most of the network data not only describes the connections of network nodes but also describes the properties of the nodes. In this paper, we propose a community detection method collects relevant evidences from the information of node attributes and the information of network structure to assist the community

Abstract In this paper, we design an innovative and general class of modified two-stage sampling schemes to enhance double sampling and modified double sampling procedures. Under the squared error loss plus linear cost of sampling, we revisit the classic problem of minimum risk point estimation (MRPE) for an unknown normal mean μ ( ∈ R ) when the population variance σ 2 ( ∈ R + ) also remains unknown

Abstract In this paper, we consider the empirical likelihood confidence intervals for mean difference under the assumption of missing at random (MAR). We prove that the empirical log-likelihood ratio of mean difference converges to a standard chi-squared distribution. We conduct simulation study to compare the proposed empirical likelihood with the normal approximation method in terms of the coverage