We propose a scaled linear mixed model to assess the effects of exposure and other covariates on multiple continuous outcomes. The most general form of the model allows a different exposure effect for each outcome. An important special case is a model that represents the exposure effects using a...

In this paper, we define different types of estimators for the distribution function, namely preliminary test (PT), shrinkage PT (SPT), Stein type (S), and Thompson shrinkage (TS) estimators based on lower record observations and their inter record times. Their asymptotic distributional bias and...

A new class of distribution-free tests for the k-sample location problem based on the linear combination of two-sample U-statistics proposed by Ozturk (2001) is proposed. Asymptotic distribution of the test statistic is obtained and the optimal weights are also determined. AREs of the proposed...

Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under α-stable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under...

In a recent paper Gonzalez Manteiga and Vilar Fernandez (1995) considered the problem of testing linearity of a regression under MA structure of the errors using a weighted L1-distance between a parametric and a nonparametric fit. They established asymptotic normality of the corresponding test...

Three nonparametric tests for several sample scale problem against umbrella alternative, when the peak of the umbrella is known, are proposed. The proposed statistics have the advantage of not requiring the several distribution functions to have a common median, but rather any common quantile of...