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-List Of Titles -Stable computation of maximum likelihood estimates in identity link Poisson regression

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/114778

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Title
Stable computation of maximum likelihood estimates in identity link Poisson regression
Related
Journal of computational and graphical statistics, Vol. 19, Issue 3 (2010), p.666-683
DOI
10.1198/jcgs.2010.09127
Publisher
American Statistical Association
Date
2010
FoR/RFCD Code(s)
010400 Statistics
Author/Creator
Marschner, Ian C
Description
Identity link Poisson regression is useful when the mean of a count variable depends additively on a collection of predictor variables. It is particularly important in epidemiology, for modeling absolute differences in disease incidence rates as a function of covariates. A complication of such models is that standard computational methods for maximum likelihood estimation can be numerically unstable due to the nonnegativity constraints on the Poisson means. Here we present a straightforward and flexible method that provides stable maximization of the likelihood function over the constrained parameter space. This is achieved by conducting a sequence of maximizations within subsets of the parameter space, after which the global maximum is identified from among the subset maxima. The method adapts and extends EM algorithms that are useful in specialized applications involving Poisson deconvolution, but which do not apply in more general regression contexts. As well as allowing categorical and continuous covariates, the method has the flexibility to accommodate covariates with an unspecified isotonic form. Its computational reliability makes it particularly useful in bootstrap analyses, which may require stable convergence for thousands of implementations. Computations are illustrated using epidemiological data on occupational mortality, and biological data on crab population counts. This article has supplementary material online.
Description
18 page(s)
Subject Keyword
010400 Statistics
Subject Keyword
EM algorithm
Subject Keyword
generalized linear model
Subject Keyword
isotonic regression
Subject Keyword
parameter constraint
Subject Keyword
rate difference
Resource Type
journal article
Organisation
Macquarie University. Dept. of Statistics

Identifier
http://hdl.handle.net/1959.14/114778
Identifier
ISSN:1537-2715
Identifier
mq-rm-2010000345
Language
eng
Reviewed
Reviewed
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Citation Format
E-mail Address
Subject
"Journal of computational and graphical statistics"
 
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