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Saddlepoint approximation at the edges of a conditional sample space

✍ Scribed by John E. Kolassa

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
148 KB
Volume
50
Category
Article
ISSN
0167-7152

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✦ Synopsis

Saddlepoint methods present a convenient way to approximate probabilities associated with canonical su cient statistic vectors in generalized linear models. Implementing saddlepoint approximations requires calculating maximum likelihood estimators for the associated parameters. When the su cient statistic vector lies at the edge of the sample space, maximum likelihood estimators may not exist. This paper describes how to modify saddlepoint approximation to work in these cases.

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