A certain locally most powerful test for one-way random effects model: full distribution and power considerations

We investigate the properties of the locally most powerful nonparametric criterion against logistic alternatives developed by Govindarajulu (1975) for testing one-way random effects modcls. We deduce the appropriate computational forms for the test criterion T and tabulate the critical values of T for α = .01, .05 and 0.10, and various sample sizes. Certain features of the computational methods are discussed. In the tables we retain only those sample sizes beyond which the asymptotic theory is meaningful. We also study the power comparison of the test for two populations with the classical F-test under a range of normal alternatives.     

Bayes and empirical bayes estimation of survival function under progressive censoring

The purpose of this note is to derive the Bayes and the empirical Bayes estimators of an unknown survival function F under progressively censored data with respect to the squared error loss function and a Dirichlet process prior using the fact that the posterior distribution of F given the data is a mixture of Dirichlet processes, and the assumption that the survival and the censor in0- distributions are continuous.     

On the maximal inequalities for the average of pairwise i.i.d. random variables

We obtain two sided inequalities for the tail of the maximal function of the averages of a multiple sequence of pairwise i.i.d. random variables taking values in a separable Banach space. We then use the results to establish a necessary and sufficient con¬dition, in terms of the common distribution of the norm of the random variables, for the maximal function to be in L , 1< p << &z.rdang;     

Parameter estimation based upon nonparametric function estimators

By considering the solution to a linear approximation of a nonlinear regression problem, a procedure for developing a para¬meter estimator, based upon a nonpammetric estimator of a para¬metric function, is given. The resulting estimators, which are determinable in closed form, are asymptotically normally distri¬buted and are optimal among the class of estimators based upon the function estimator. Further, in many cases, the estimator will have the same asymptotic distribution theory as the correspond¬ing maximum likelihood estimator. Estimators based upon the Kaplan-Meier quantile function are developed for randomly censored samples.     

On the hyper-dirichlet type 1 and hyper-liouville distributions

This paper concerns the characterization of a new family of multivariate beta distribution functions - the hyper-Dirichlet type 1 distribution. This family describes the joint density function of the terminal variates of an arbitrary tree constructed from finite sequences of probability vectors having independent Dirichlet type 1 distributions. Expressions for the general properties of the hyper-Dirichlet type 1 distribution are presented. In addition, the hyper-Liouville distribution is described and its properties are discussed as well as a generalization of the Liouville integral identity.     

On some special birth processes in biology

This paper presents some theorems on distributions associated with some special birth processes in biology.     

Estimation of size and mean of a sensitive quantitative variable for a sub-group of a population

In the present paper, a new problem in survey sampling has been considered and an attempt has bcen made to find its solution. The procedures available in literature deal with the estimation of mean of a sensitive quantitative variable for the whole population. Sometimes the investigator may be interested in estimating the mean of a sensitive quantitative variable for a sub-group of the population. At the same time, he/she may also be interested in estimating the size of this sub-group. In this paper, we have obtained estimators of these parameters. Approximate bias and variance expressions are also obtained for these estimators.     

Distribution theory of quadratic forms for matrix multivariate elliptical distribution

This paper proposes the density and characteristic functions of a general matrix quadratic form X^(?)AX${\mathbf{X}}^{(?)}\mathbf{AX}$, when A=A^(?)$\mathbf{A}={\mathbf{A}}^{(?)}$ is a positive semidefinite matrix, X$\mathbf{X}$ has a matrix multivariate elliptical distribution and X^(?)${\mathbf{X}}^{(?)}$ denotes the usual conjugate transpose of X$\mathbf{X}$. These results are obtained for real normed division algebras. With particular cases we obtained the density and characteristic functions of matrix quadratic forms for matrix multivariate normal, Pearson type VII, t and Cauchy distributions.