Normal-distribution-based maximum likelihood (NML) is most widely used for missing data analysis although real data seldom follow a normal distribution. When missing values are missing at random (MAR), recent results indicate that NML estimates (NMLEs) are still consistent for nonnormally...

This article focuses on the estimation of the parametric component, which is of primary interest, in semi-varying coefficient models with heteroscedastic errors. Specifically, we first present a procedure for estimating the variance function of the error term and the resulting estimator is...

In this paper, problems of linearly admissible estimators for stochastic regression coefficients are considered in a general Gauss–Markoff model with random effects. The generalized balanced loss function is given, and under it the admissibility of linear estimators is investigated. Sufficient...

Many clustering techniques aim at optimizing empirical criteria that are of the form of a U-statistic of degree two. Given a measure of dissimilarity between pairs of observations, the goal is to minimize the within cluster point scatter over a class of partitions of the feature space. It is the...

In this article we derive an optimal test for testing the significance of covariance matrices of random-effects of two multivariate mixed-effects linear models. We compute the power of this newly derived test via simulation for various alternative hypotheses in a bivariate set up for unbalanced...

It is crucial to check validation of any statistical model after fitting it for a given set of data. In Bayesian statistics, a researcher can check the fit of the model using a variety of strategies. In this paper we consider two major aspects, first checking that the posterior inferences are...

The growth curve model is a useful tool for studying the growth problems, repeated measurements and longitudinal data. A key point using the growth curve model to fit data is determining the degree of polynomial profile form, choosing suitable explanatory variables, shrinking some regression...

Directional regression is an effective sufficient dimension reduction method which implicitly synthesizes the first two conditional moments. In this paper, we extend directional regression to a general family of estimators via the notion of general empirical directions. Data-driven method is...

In the context of the Fay–Herriot model, a mixed regression model routinely used to combine information from various sources in small area estimation, certain adjustments to a standard likelihood (e.g., profile, residual, etc.) have been recently proposed in order to produce strictly positive...

We introduce the concept of subsignature for semicoherent systems as a class of indexes that range from the system signature to the Barlow–Proschan importance index. Specifically, given a nonempty subset M of the set of components of a system, we define the M-signature of the system as the...