Inferential procedures for the difference between two multivariate normal mean vectors based on incomplete data matrices with different monotone patterns are developed. Assuming that the population covariance matrices are equal, a pivotal quantity, similar to the Hotelling T2 statistic, is...

In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d[greater-or-equal, slanted]2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two...

The problem of estimating the precision matrix of a multivariate normal distribution model is considered with respect to a quadratic loss function. A number of covariance estimators originally intended for a variety of loss functions are adapted so as to obtain alternative estimators of the...

This paper studies the estimation of a varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model [Fan and Huang, Manuscript, University of North Carolina, Chapel Hill, USA, 2002]. We focus on...

Estimation of the location parameters of a px1 random vector with a spherically symmetric distribution is considered under quadratic loss. The conditions of Brandwein and Strawderman [Ann. Statist. 19(1991) 1639-1650] under which estimators of the form dominate are (i) where -h is superharmonic,...

The distributions of the ratio X/Y are derived when (X,Y) has the elliptically symmetric Pearson-type II distribution, elliptically symmetric Pearson-type VII distribution and the elliptically symmetric Kotz-type distribution.

Model identification and discrimination are two major statistical challenges. In this paper we consider a set of models for factorial experiments with the parameters representing the general mean, main effects, and only k out of all two-factor interactions. We consider the class of all...

Chernoff and Savage [Asymptotic normality and efficiency of certain non-parametric tests, Ann. Math. Statist. 29 (1958) 972-994] established that, in the context of univariate location models, Gaussian-score rank-based procedures uniformly dominate--in terms of Pitman asymptotic relative...

We introduce regularized wavelet-based methods for nonlinear regression modeling when design points are not equally spaced. A crucial issue in the model building process is a choice of tuning parameters that control the smoothness of a fitted curve. We derive model selection criteria from an...

A new approach of estimating parameters in multivariate models is introduced. A fitting function will be used. The idea is to estimate parameters so that the fitting function equals or will be close to its expected value. The function will be decomposed into two parts. From one part, which will...