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Further remarks on asymptotic normality of likelihood and conditional analyses
Author(s) -
Fraser D. A. S.,
Mcdunnough P.
Publication year - 1984
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3314746
Subject(s) - mathematics , conditional probability distribution , normality , confidence interval , statistics , transformation (genetics) , likelihood function , asymptotic distribution , econometrics , maximum likelihood , confidence distribution , likelihood principle , function (biology) , quasi maximum likelihood , estimator , biochemistry , chemistry , gene , evolutionary biology , biology
Under weak conditions the normalized likelihood with or without weight function almost surely converges to a normal density function: for a real parameter or a vector parameter; with or without the assumption of independent identical distributions. Applications arise for confidence intervals, confidence distributions, structural distributions. and conditional analyses with transformation and structural models.