Some multivariate linear regression testing problems with additional observations
Sanat K. Sarkar,Sanat K. Sarkar +1 more
TLDR
In this paper, Sarkar et al. extended this idea to cover multivariate linear regression testing problems with the same type of additional observations on a set of correlated auxiliary variables and proposed a similar test for a mean testing problem with additional observations.About:
This article is published in Journal of Multivariate Analysis.The article was published on 1981-12-01 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Bayesian multivariate linear regression & Multivariate statistics.read more
Citations
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Book ChapterDOI
Inference on Parameters of Multivariate Normal Populations When Some Data is Missing
TL;DR: In this paper, an approach via the completeness of an exponential family to obtain a uniformly most powerful unbiased (UM PU) test or simple similar or unbiased tests is presented. But in practice, such a situation is not always realized, because of the inherent nature of the population sampled, as with skeletal material, in which not all observations can be taken on each specimen or the nature of phase sampling employed.
Journal ArticleDOI
Bayesian Inference in Multivariate Regression With Missing Observations on the Response Variables
Irwin Guttman,Ulrich Menzefricke +1 more
TL;DR: In this paper, a multivariate linear model with missing observations in a nested pattern is discussed, where the predictive density of the missing observations is taken into account in determining the posterior distribution of B and its mean and variance matrix.
Journal ArticleDOI
A note on a result for two SUR models
Arjun K. Gupta,D. G. Kabe +1 more
TL;DR: In this paper, the covariance matrices of the Aitken estimators of the regression coefficients parameter matrix of two SUR models were derived by using a known result in multivariate statistical analysis.
Journal ArticleDOI
A solution to multiple linear regression problems with ordered attributes
TL;DR: In this article, a class of multiple linear regression techniques is discussed, in which the order of magnitude is constrained among regression coefficients, and each predictor variable is a qualitative variate having some categories which are on an ordinal scale.
Journal ArticleDOI
On a multivariate normal linear regression hypothesis testing problem with additional observations
TL;DR: In this paper, a slightly different ad hoc nuisance parameter removal test for this problem, whose derivation is based on generalized Sverdrup's lemma, Kabe (1965) is presented.
References
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Journal ArticleDOI
On Solutions of the Behrens-Fisher Problem, Based on the $t$-Distribution
Journal ArticleDOI
Strong Consistency of Least Squares Estimates in Normal Linear Regression
T. W. Anderson,John B. Taylor +1 more
TL;DR: In the usual linear regression model, sample regression coefficients converge with probability one to the population regression coefficients when the dependent variables are normally distributed and the inverse of the second-order moment matrix of the independent variables converges to the zero matrix.
Journal ArticleDOI
Asymptotic theory of likelihood ratio and rank order tests in some multivariate linear models
Pranab Kumar Sen,Madan L. Puri +1 more
TL;DR: In this paper, the authors develop the asymptotic distribution theory of the normal theory likelihood ratio test statistic for the multivariate general linear hypothesis problem when the parent distribution is not necessarily normal.
Book ChapterDOI
16 Likelihood ratio tests for mean vectors and covariance matrices
P.R Krishnaiah,Jack C. Lee +1 more
TL;DR: In this article, the authors describe various likelihood ratio tests on mean vectors and covariance matrices and discuss computations of the critical values associated with these tests and discuss the applications of the tests on linear structures in the area of the components of variance.
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