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SIMULATED MAXIMUM LIKELIHOOD APPLIED TO NON‐GAUSSIAN AND NONLINEAR MIXED EFFECTS AND STATE–SPACE MODELS
Author(s) -
Millar Russell B.
Publication year - 2004
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2004.00352.x
Subject(s) - mathematics , maximum likelihood , statistics , maximum likelihood sequence estimation , restricted maximum likelihood , random effects model , binary data , mixed model , expectation–maximization algorithm , quasi likelihood , likelihood function , state space , marginal likelihood , negative binomial distribution , binary number , poisson distribution , medicine , meta analysis , arithmetic
Summary The paper presents an overview of maximum likelihood estimation using simulated likelihood, including the use of antithetic variables and evaluation of the simulation error of the resulting estimates. It gives a general purpose implementation of simulated maximum likelihood and uses it to re‐visit four models that have previously appeared in the published literature: a state–space model for count data; a nested random effects model for binomial data; a nonlinear growth model with crossed random effects; and a crossed random effects model for binary salamander‐mating data. In the case of the last three examples, this appears to be the first time that maximum likelihood fits of these models have been presented.