In this work, we extend standard likelihood-based procedures to the multivariate linear model using the scale mixtures of multivariate skew-normal-Cauchy distributions. A simple EM algorithm for iteratively computing maximum likelihood estimates is derived. The observed information matrix is computed analytically to account for standard errors. Some results are obtained from real and simulated datasets

In this work, the volatility processes of the double Heston model are extended to treat the long memory property of the volatility. In this article, we study our presented model as a new version of the two-factor stochastic volatility model that it is proposed to model the volatility by a mean reverting equation driven by fractional Brownian motion named fractional Cox–Ingersoll–Ross process. Next

Nonlinear mixed-effects models allow modeling repeated measures over time. The fixed effects of these models allow incorporating covariates, whereas the random effects reflect the multiple sources of heterogeneity and correlation between and within the units. To estimate the parameters of these models, it is necessary to use iterative processes, which can be done through different approaches, some

Ridge regression is an alternative to the ordinary least squares method when multicollinearity presents among the regressor variables in multiple linear regression analysis. The selection of the ridge parameter is an important issue to obtain a good performance of the ridge regression. In this article, a new method is proposed for determining the ridge parameter in ridge regression. This method is

Abstract In numerous application areas, when the response variable is continuous, positively skewed, and well fitted to the inverse Gaussian distribution, the inverse Gaussian regression model (IGRM) is an effective approach in such scenarios. The problem of multicollinearity is very common in several application areas like chemometrics, biology, finance, and so forth. The effects of multicollinearity

We develop a constrained indirect inference approach based on a wrapped skewed-t auxiliary model for the estimation of the wrapped stable distribution. To improve the finite-sample properties of the estimators, we devise a bootstrap-based estimate of the weighting matrix employed in the indirect inference program. The simulation study suggests that, in terms of root-mean-squared-error, the indirect

In a clustered point pattern, consisting of parent and offspring points, in order to study the attributes of parent points as well as the behavior of offspring points which are scattered around the parents, one needs to know the locations of parent points generating offsprings. In this paper we use the Delaunay triangulation and present a general method to detect parent locations. The performance of

We consider the parameter estimation for multiplicative linear regression models with or without multiplicative distortion measurement errors. For the latter, both the response variable and the covariates are are unobserved and distorted by unknown functions of a commonly observable confounding variable. With or without distortion measurement errors, we propose the general least product relative error

A new formulation for the proportion of true null hypotheses ( π 0 ) , based on the sum of all p-values and the average of expected p-values under the false null hypotheses has been proposed in the current work. This formulation of the parameter of interest π 0 has also been used to construct a new estimator for the same. The proposed estimator removes the problem of choosing tuning parameters in the

In this study, we propose a standardized likelihood ratio test for homogeneity of variances of several normal populations. We obtain the exact mean and variance of the likelihood ratio test. The proposed test, which does not require intensive simulation, is quite easy to implement especially for researchers. Furthermore, we derive the null distribution of the likelihood ratio test via Monte Carlo simulation

Assuming that a two-stage serial-parallel processing system is an M/G/m queuing system. Firstly, let sojourn time of the order in one stage is the system state, and the state space equation is used to model this system. Also, the influence of the mean shift of sojourn time on the downstream stage is analyzed. Secondly, the average sojourn time is calculated. And the distribution function of the sojourn

The inverse Gaussian regression model (IGRM) is frequently applied in the situations, when the response variable is positively skewed and well fitted to the inverse Gaussian distribution. The maximum likelihood estimator (MLE) is generally used to estimate the unknown regression coefficients of the IGRM. The performance of the MLE method is better if the explanatory variables are uncorrelated with

In this article, we discuss the regression analysis of dependent current status data under the accelerated failure time model. There exist many literatures discussing the regression analysis of current status data under different models, but few literature discussing the regression problem of dependent current status data under the AFT model. Corresponding to this, we propose a sieve maximum likelihood

A profile is a relationship between the response variable(s) and the independent variable(s), which describes the quality of a process or product. The profile can be monitored by a process control chart, which is an important tool in the statistical process control. Using the robust estimators in monitoring profiles in the presence of contamination is so effective, and it improves the efficiency of

The scoring processes of home and away team in basketball games are modeled by two dependent inverse Gaussian processes with a team-specific parameter that measures the team strength. A common latent variable that measures the game pace is designed to characterize the dependence. A moment estimation method combined with maximum likelihood estimation is proposed to fit the parameters and a Bayesian

In some quality control applications, quality of a product or process may be characterized effectively by a functional relationship between one or more variables that is typically referred to as a profile. In this paper, we propose four control charts, namely the maximum CUSUM (MaxCUSUM), maximum Crosier CUSUM (MaxCCUSUM), maximum EWMA (MaxEWMA) and maximum double EWMA (MaxDEWMA) charts, for monitoring

The local influence analysis is useful for identifying influential observations in statistical diagnostics and sensitivity analysis. However, it is often criticized for lack of a rigorous criterion to judge the influence magnitude from the elements of the main diagnostic. In this paper, a new method, call sparse local influence analysis, is proposed to detect the influential observations. We establish

The purpose of this article is the statistical surveillance of spatial autoregressive models, where the observed process is monitored over both space and time. The considered spatial model contains disturbances with heavy tails. The control procedures based on exponential smoothing or cumulative sums are constructed using characteristic quantities including the first and the second moments to monitor

A Gaussian Copula-based regression model is proposed that accounts for associations between count responses with extra zeros. Our approach entails underlying latent variables to indicate the latent mechanisms which generate the count responses where some of the count responses are inflated in a zero point. The model contains, as special sub-models, several important distributions such as the power

In this paper, we develop testing procedures for hypotheses regarding the difference of location parameters from two independent two-parameter exponential populations with unknown but proportional scale parameters. We design appropriate two-stage and purely sequential procedures to determine the appropriate sample sizes while controlling both type-I and type-II error probabilities at or below the preassigned

In this paper, we consider the CUSUM estimator based on α-mixing sequences. By giving the consistency estimators for mean and covariance functions, the limit distribution of CUSUM estimator is presented as a standard Brownian bridge, which can be used in the change point detection. As applications, some simulations and real data examples are illustrated to test our CUSUM estimator.

Quick detection of common changes is critical in sequential monitoring of multi-stream data where a common change is referred as a change that only occurs in a portion of panels. After a common change is detected by using a SR-SUM procedure, we compare the CUSUM and EWMA methods in terms of FDR and FNR after a common change is detected. The BH-type procedure is used for both CUSUM processes and EWMA

We consider the problem of variable selection in regression models. In particular, we are interested in selecting explanatory covariates linked with the response variable and we want to determine which covariates are relevant, that is which covariates are involved in the model. In this framework, we deal with L 1-penalized regression models. To handle the choice of the penalty parameter to perform

In this note, we develop a novel algorithm for generating random numbers from a distribution with a probability density function proportional to  sin k(x),x∈(0,π) and k≥1. Our algorithm is highly efficient and is based on rejection sampling where the envelope distribution is an appropriately chosen beta distribution. An example application illustrating how the new algorithm can be used to generate

Statistical models often rely on several assumptions including distributional assumptions on outcome variables as well as relational assumptions representing the relationship between outcomes and independent variables. Model diagnostics is, therefore, a crucial component of any model fitting problem. Residuals play important roles in model diagnostics and checking assumptions. In multivariate models

In many large-scale survival studies, some features are known to be truly predictive of the survival time among massive covariates in advance. To make full use of this prior information, we propose a new model-free conditional feature screening procedure by extending the MC screening method via inverse probability censoring weighting. The newly suggested method has at least three appealing merits:

This article introduces a new family of bivariate generalized linear exponential (BGLE) distributions, whose marginals are generalized linear exponential (GLE) distributions. We derive the expressions for regression function, product moments and the stress–strength parameter P ( X < Y ) for the BGLE distribution. Several statistical properties, a local dependence function and some association measures

Tweedie’s compound Poisson model is a popular method to model insurance claims with probability mass at zero and nonnegative, highly right-skewed distribution. In particular, it is not uncommon to have extremely unbalanced data with excessively large proportion of zero claims, and even traditional Tweedie model may not be satisfactory for fitting the data. In this paper, we propose a boosting-assisted

We addressed the problem of estimating regression coefficients for partially linear models, where the nonparametric component is approximated using smoothing splines and subspace information is available. We proposed pretest and shrinkage estimation strategies using the profile likelihood estimator as the benchmark. We examined the asymptotic distributional bias and risk of the proposed estimators

We propose a family of distributions as an alternative for a recent compound unimodal distribution for modeling insurance losses. The family of distributions, referred to as density-hazard distributions, has closed form density and distribution functions, hence easier to fit and simulate from. The distributions also show good adherence to insurance loss data and estimates risk measures relatively closely

The exact null distributions of test statistics used for testing up to k ( ≥ 1 ) upper outliers in a two-parameter Laplace sample are investigated. Two types of test statistics, namely, the modified Murphy’s test for k upper normal outliers and the general Dixon-type test statistic discussed by Childs, are considered. Utilizing the result of conditional independence of blocked ordered data established

Interval-censored survival data arise naturally in many fields such as medical follow-up studies, in which the event or failure is not observed exactly but only known to occur within a time interval. Most existing approaches for analyzing interval-censored failure time data assume that the examination times and the failure time are independent or conditionally independent given covariates. While this

In a high-dimensional linear regression model, this article is concerned with testing statistical significance of a subset of regression coefficients. The conventional partial F-test is not applicable in high-dimensional situations. Several methods for testing whether any of the discarded covariates is significant conditional on relative importance of predictors have been proposed in the recent literature

We propose an improved randomized response model for the simultaneous estimation of population means of two quantitative sensitive variables by using blank card option that make use of one scramble response and another fake response. The properties of the proposed estimator have been analyzed and empirical studies are performed to show its dominance over existing estimators along with enhance privacy

This paper is concerned with feature screening for the ultrahigh dimensional survival data. We propose a new feature screening procedure by extending the method of Zhu et al. via inverse probability censoring weighting. The proposed procedure enjoys two appealing merits. First, it does not need to specify any model assumption between the response and the covariates. Thus, it is robust to the model

Systems in engineering usually not only have multiple states but also may be required to fulfill several functions, meaning the system has multiple performance variables in a specific state. In a multi-performance multi-state weighted K −-out-of-n system, the system consists of several multi-performance multi-state components for completing its different functions. As this system is more complicated

For many years, authors have been interested in developing methods for generating distributions that provide a flexible family to model lifetime variables. This paper proposes an exact inference approach to the family of proportional reversed hazard distributions based on the pivotal quantity, which yields exact confidence intervals with the shortest-length as well as reasonable estimators for the

In order to show the effect of treatment, the change between two repeated psychometric measurements at the individual level should be estimated. The simplest method is to calculate the absolute difference between two measurements. However, measurements obtained in a clinical setting are often influenced by other changes not related to the treatment. One of the typical sources of error is regression

The traditional network reliability research generally focuses on the topological connectivity among given nodes based on graph theory. Without considering the relationship between network failures and network performance degradation, traditional network reliability analysis cannot reflect actual network ability reaching up to a certain accomplishment level over a utilization interval. As for the performability

A double moving average (DMA) control chart has been proposed in the literature for monitoring the process mean. Several studies have also been done based on this scheme. Unfortunately, the variance of DMA statistic that has been used in these studies is not correct. In this article, we provide the correct variance of the DMA chart and through a simulation study, we evaluate its performance. It is

Sousa et al. and Gupta et al. presented ratio and regression estimators of population mean of a sensitive variable using auxiliary information in simple random sampling without replacement. In this article, we propose a generalized randomized response technique (RRT) model and use it to develop some exponential estimators in two-phase sampling. We also discuss the privacy protection level of the proposed

EWMA and Max-EWMA charts are considered efficient for individual as well as joint monitoring of mean and variance shifts in the production process. However, measurement error is affecting the efficiency of these charts. In this study we propose a maximum exponentially weighted moving average with measurement error using auxiliary information control chart and name it Max-EWMAMEAI control chart. The

In this article, we first discuss a few properties along with limitations of traditional measure of coefficient of dispersion in comparison to the well-known standard measure of variation called the coefficient of variation. To overcome the limitations in the traditional coefficient of dispersion, a new measure of coefficient of dispersion is introduced which is more informative than the conventional

A simulation study is done to compare Ridge Regression (RR) and the Lasso, under the assumption of a linear model, by calculating four metrics: the squared distance, from the true coefficients, of estimated coefficients that are both statistically significant and true; the proportion of true regressors discovered; the squared distance, from the true predictions, of the predictions made using the estimated

We consider the confidence interval for the univariate linear calibration, where a response variable is related to an explanatory variable by a simple linear model, and the observations of the response variable and known values of the explanatory variable are used to make inferences on a single unknown value of the explanatory variable. Since the univariate linear calibration suffers from a problem

In studies related to social or medical sciences, ordinal responses are often recorded repeatedly over time on a subject. A semi-parametric model with spline smoothing has been considered to capture the temporal trend exhibited in the longitudinal data. In addition, information on covariates and/or responses may not be available in one or more visit. A dynamic model for both missing responses and covariates

In this paper, we consider the one-sided hypothesis testing problems for variance component functions in the unbalanced two-way random effects model. Firstly, using the Bootstrap approach and generalized approach, the test statistics and confidence intervals for the sum and ratio of variance components are constructed respectively. Next, the Monte Carlo simulation results indicate that the Bootstrap

In this paper, the statistical inference on the location parameter vector is studied in the multivariate skew-normal model with unknown scale parameter and known shape parameter. Based on the distribution of the generalized Hotelling’s T 2 statistic, confidence regions and hypothesis tests on the location parameter, μ , are obtained. The power function of the test is studied numerically as well. For

In the multivariate statistics community, it is commonly acknowledged that among the hierarchical clustering tree (HCT) procedures, the single linkage rule for inter-cluster distance, tends to produce trees which are significantly more asymmetric than those obtained using other rules such as complete linkage, for instance. We consider the use of Shannon’s entropy of the partitions determined by HCTs

We present a new method of testing for unit roots in the INAR(1) model based on estimated variances. We present detailed simulation evidence regarding the performance of the new test statistics that show that our method is more powerful than the Dickey–Fuller tests especially in nearly unit root circumstances. We evaluate the presence of a unit root in two empirical time series, namely, the number

We propose a new semiparametric regression model with exponentiated power exponential errors using the B-spline basis for nonlinear effects. We adopt the framework of the generalized additive models for location, scale, and shape to fit this regression model. We obtain the maximum penalized likelihood estimates of the model parameters by considering nonlinear effects. Some global-influence measurements

This paper deals with the study of some probabilistic and statistical properties of some particular models of the class of Periodic Integer-Valued Autoregressive Moving Average, PINARMA(p, q), Models. For any considered particular model, the necessary and sufficient conditions for the periodically stationary in the first and second order, are established. The closed forms of the mean and the variance

Missing data are common in medical and social science studies and often face a serious challenge in ultrahigh-dimensional data analysis. In this paper, a nonparametric feature screening approach based on the imputation technique is proposed for ultrahigh-dimensional data with responses missing at random, where the imputation technique is used to replacing each missing value with a set of plausible

Finite mixture model-based clustering is a popular method to perform unsupervised learning, however the classic approach for parameter estimation, the expectation-maximization algorithm, is quite susceptible to converging to, at times extremely poor, local maxima. Recently, swarm-based algorithms have gained popularity in a number of optimization scenarios. One such swarm-based optimization procedure

Developing a robust, accurate forecasting model and improving the prediction abilities of the limited historical data that lacks statistical rules has become a top priority. To address this problem, an improved conformable fractional non-homogeneous gray model, namely CFONGM(1,1,k,c), is proposed. Combining the dynamic background-value and particle swarm optimization algorithm to further improve forecasting

In this paper, a class of hybrid type estimators is proposed in estimating the finite population variance using single auxiliary variable in simple random sampling. Expressions for the bias and the mean square error (MSE) are derived up to the first order of approximation. Theoretically comparisons are provided to show that the proposed estimators perform more efficiently than various existing estimators

In this paper, we first discuss some of the well-known methods available in the literature for the estimation of the parameters of a univariate/multivariate stable distribution. Based on the available methods, a new hybrid method is proposed for the estimation of the parameters of a univariate stable distribution. The proposed method is further used for the estimation of the parameters of a strictly

Varying-coefficient models (VCMs) are widely used in a variety of statistical applications. However, the classical VCMs based on least squares are prone to the presence of even a few severe outliers. In this article, a mean shift parameter is added for each observation to reflect outliers, and different penalties are then applied to the shift parameters to get sparse estimates. The jointly penalized

We derive a procedure to obtain the exact probability that a specific pattern of letters occurs in a longer random sequence of letters. The procedure is generalized to find the exact probability of a fixed (specific) single pattern, and a union or intersection of multiple fixed (specific) patterns within a random sequence perfectly for any distributions of a cell in the random sequence, and can handle

In this paper, an attri-vari inspection policy for the resubmitted lots based on the process capability index Cpk is proposed. The proposed sampling plan comprises of both attribute and variables inspections for the resubmitted lots using single sampling plan. In the case of variables inspection, both symmetric and asymmetric fraction non conforming cases have been considered. Tables are developed