Linear regression models are studied when variables of interest are observed in the presence of measurement error. Techniques involving Fourier transforms that lead to simple differential equations with unique solutions are used in the context of multiple regression. Necessary and sufficient...

We consider the multivariate Farlie-Gumbel-Morgenstern class of distributions and discuss their properties with respect to the extreme values. This class was used to consider dependence in multivariate distributions and their ordering. We show that the extreme values of these distributions...

We consider the problem of decision-theoretic estimation of the ratio of generalized variances of two matrix normal distributions with unknown means under a general loss function. The inadmissibility of the best affine equivariant estimator is established by exhibiting various improved...

Additive principal components(APCs) generalize classicalprincipal component analysisto additive nonlinear transformations.Smallest APCsare additive functions of the vectorX=(X1, ..., Xp) minimizing the variance under orthogonality constraints and are characterized as eigenfunctions of an...

A new methodology is introduced for spatial sampling design when the variable of interest cannot be directly observed, but information on it can be obtained by sampling a related variable, and estimation of the underlying model is required. An approach based on entropy has been proposed by...

By establishing the asymptotic normality for the kernel smoothing estimator[beta]nof the parametric components[beta]in the partial linear modelY=X'[beta]+g(T)+[var epsilon], P. Speckman (1988,J. Roy. Statist. Soc. Ser. B50, 413-456) proved that the usual parametric raten-1/2is attainable under...

Dawid (1973,Biometrika60, 664-666) stated conditions in the univariate location model with known scale parameter needed for there to be either vanishing likelihood or prior influence on the posterior distribution when there is a conflict between likelihood and prior. More recently, Pericchi and...

Nonparametric factorial designs for multivariate observations are considered under the framework of general rank-score statistics. Unlike most of the literature, we do not assume the continuity of the underlying distribution functions. The models studied include general repeated measures...

Theories of nonparametric regression are usually based on the assumption that the design density exists. However, in some applications such as those involving high-dimensional or chaotic time series data, the design measure may be singular and may be likely to have a fractal (nonintegral)...

In this paper we are concerned with Bayesian statistical inference for a class of elliptical distributions with parameters[mu]and[Sigma]. Under a noninformative prior distribution, we obtain the posterior distribution, posterior mean, and generalized maximim likelihood estimators...