# Muni S. Srivastava

In this paper, tests are developed for testing certain hypotheses on the covariance matrix Σ, when the sample size N = n+1 is smaller than the dimension p of the data. Under the condition that (trΣ/p) exists and > 0, as p → ∞, i = 1, . . . , 8, tests are developed for testing the hypotheses that the covariance matrix in a normally distributed data is an(More)
In microarray experiments, the dimension p of the data is very large but there are only few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of the two groups, when p is large, is considered. Three procedures based on Moore-Penrose inverse of the sample covariance matrix and an empirical Bayes estimate(More)
• J. Multivariate Analysis
• 2013
In this paper we propose a test for testing the equality of the mean vectors of two groups with unequal covariance matrices based on N1 and N2 independently distributed p-dimensional observation vectors. It will be assumed that N1 observation vectors from the first group are normally distributed with mean vector μ1 and covariance matrix Σ1. Similarly, the(More)
• 3
• J. Multivariate Analysis
• 2010
In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained frommany clusters. As the variable selection procedure, we here use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds(More)
• J. Multivariate Analysis
• 2012
We consider two hypothesis testing problems with N independent observations on a single m-vector, when m > N , and the N observations on the random m-vector are independently and identically distributed as multivariate normal with mean vector μ and covariance matrix Σ, both unknown. In the first problem, the m-vector is partitioned into two subvectors of(More)
When families have different numbers of offspring, the maximum likelihood procedure for estimating the intraclass correlation is iterative, requiring considerable computation. Occasionally, the iterations do not even converge. To overcome this difficulty, several non-iterative estimators have been proposed by Smith (1956). However, to choose from among(More)
• Computational Statistics & Data Analysis
• 2016
The problem of estimating the large covariance matrix of both normal and nonnormal distributions is addressed. In convex combinations of the sample covariance matrix and the identity matrix multiplied by a scalor statistic, we suggest a new estimator of the optimal weight based on exact or approximately unbiased estimators of the numerator and denominator(More)