Estimation and hypothesis testing for a nonnormal bivariate distribution with applications

In numerous situations, one deals with a random vector ( X, Y), where Y is a consequence of X but not so much the other way round. Often in such situations, X has a nonnormal distribution while the conditional distribution of Y given X = x may or may not be normal. In this paper, we assume the distr...

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Bibliographic Details
Published inMathematical and computer modelling Vol. 32; no. 1; pp. 53 - 67
Main Authors Vaughan, D.C., Tiku, M.L.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.07.2000
Subjects
Correlation coefficient
Extreme value
Maximum likelihood
Modified maximum likelihood
Weibull
Correlation coefficient
Weibull
Maximum likelihood
Extreme value
Modified maximum likelihood
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Summary:In numerous situations, one deals with a random vector ( X, Y), where Y is a consequence of X but not so much the other way round. Often in such situations, X has a nonnormal distribution while the conditional distribution of Y given X = x may or may not be normal. In this paper, we assume the distribution of X to be the extreme value distribution and the conditional distribution of Y to be normal. We derive the MML (modified maximum likelihood) estimators and show that they are highly efficient. We also develop hypothesis testing procedures.
ISSN:0895-7177
1872-9479
DOI:10.1016/S0895-7177(00)00119-9