Titlebook: Statistical Distributions in Scientific Work; Volume 5 — Inferenti Charles Taillie,Ganapati P. Patil,Bruno A. Baldess Book 1981 D. Reidel P

FOLLY

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Conditional Maximum Likelihood Estimation in Gaussian Mixturespriate since the likelihood function is unbounded at each sample point. However, this does not seem to cause serious problems when iterative methods are used on a computer. This phenomenon is partially explained by the conditional likelihood approach taken in this paper. In addition, the conditional

CHURL

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On the Asymptotic Distribution of the Multivariate Cramer-Von Mises and Hoeffding-Blum-Kiefer-Rosenbmultivariate Cramér-von Mises statistic. The second part is a preview of some developments in the asymptotic distribution theory of the Hoeffding-Blum-Kiefer-Rosenblatt independence criterion in that the there quoted 1980 joint results with Derek S. Cotterill of the Department of national Defence, O

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CRACY

On Tests of Independence under Bivariate Exponential Modelsussion then turns to the bivariate exponential distribution (BVE) of Marshall and Olkin (1967) and the absolutely continuous bivariate exponential distribution (ACBVE) of Block and Basu (1974). Power calculations are carried out for several statistics, and asymptotic relative efficiencies are calcul

Detain

On Tests for Detecting Change in the Multivariate Meanhe alternative that a change has occurred at some unknown point r, (i.e., .. = .2 =… = .. ≠ .. =…= ..). The ..’s are assumed to be normally distributed with common unknown covariance. An estimate of the change point r is also given.

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A Two-Dimensional T-Distribution and a New Test with Flexible Type I Error Controlthe possibility that one of the component hypotheses may be principal, i.e., scientifically more relevant or important than the other. In this we use some properties of a bivariate t-distribution to construct a test which permits asymmetric treatment of the two component hypotheses. The proposed tes

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Testing Outliers in Multivariate Datations are outliers (too discordant as compared to the bulk of observations). As a generalization of Tiku’s (1975, 1977) univariate statistic, we propose a statistic g for testing a specified number of outliers in multivariate data; g is the ratio of the product of robust estimators (Tiku, 1980) to t

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A Normal Approximation for the Multivariate Likelihood Ratio Statistics The exact distributions needed for implementing these tests are complex and their tabulation is limited in scope and accessibility. In this paper, a method of constructing normal approximations to these distributions is described, and illustrated using the problems of testing sphericity and indepen