#### Filter Results:

- Full text PDF available (13)

#### Publication Year

1986

2016

- This year (0)
- Last 5 years (10)
- Last 10 years (17)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Muni S. Srivastava, Hirokazu Yanagihara
- J. Multivariate Analysis
- 2010

- Muni S. Srivastava, Shota Katayama, Yutaka Kano
- J. Multivariate Analysis
- 2013

- Muni S. Srivastava
- J. Multivariate Analysis
- 2009

- Muni S. Srivastava, Tatsuya Kubokawa
- J. Multivariate Analysis
- 2010

Discussion Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Discussion Papers may not be reproduced or distributed without the written consent of the author. Abstract In this paper, we consider the problem of selecting the variables of the fixed… (More)

- M S Srivastava
- Annals of human genetics
- 1993

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)

- Muni S. Srivastava, N. Reid
- 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 multivari-ate 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)

- Yuki Ikeda, Tatsuya Kubokawa, Muni S. Srivastava
- Computational Statistics & Data Analysis
- 2016

Discussion Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Discussion Papers may not be reproduced or distributed without the written consent of the author. Abstract The problem of estimating the large covariance matrix of both normal and… (More)

- Muni S. Srivastava, Tõnu Kollo, Dietrich von Rosen
- J. Multivariate Analysis
- 2011

This article analyzes whether the existing tests for the p × p covariance matrix Σ of the N independent identically distributed observation vectors with N ≤ p work under non-normality. We focus on three hypotheses testing problems: (1) testing for sphericity, that is, the covariance matrix Σ is proportional to an identity matrix I p ; (2) the covariance… (More)

- M S Srivastava, T Kubokawa
- 2005

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)

- M S Srivastava
- Biometrics
- 1986

In this paper alternative methods for estimating the relative potency, its confidence intervals, and testing for proportionality are developed for multivariate bioassays. The test and estimate are based on the smaller characteristic root and the corresponding characteristic vector of a 2 X 2 matrix. The same idea is applied in combining several symmetric… (More)