Maximization by Parts in Likelihood Inference

Peter Song (), Yanqin Fan () and John Kalbfleisch ()
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Peter Song: Department of Mathematics and Statistics, York University; Toronto, ON
Yanqin Fan: Department of Economics, Vanderbilt University
John Kalbfleisch: Department of Biostatistics, University of Michigan School of Public Health

No 319, Vanderbilt University Department of Economics Working Papers from Vanderbilt University Department of Economics

Abstract: This paper presents and examines a new algorithm for solving a score equation for the maximum likelihood estimate in certain problems of practical interest. The method circumvents the need to compute second order derivatives of the full likelihood function. It exploits the structure of certain models that yield a natural decomposition of a very complicated likelihood function. In this decomposition, the first part is a log likelihood from a simply analyzed model and the second part is used to update estimates from the first. Convergence properties of this fixed point algorithm are examined and asymptotics are derived for estimators obtained by using only a finite number of steps. Illustrative examples considered in the paper include bivariate and multivariate Gaussian copula models, nonnormal random effects and state space models. Properties of the algorithm and of estimators are evaluated in simulation studies on a bivariate copula model and a nonnormal linear random effects model.

Keywords: Copula models; fixed-point algorithm; information dominance; iterative algorithm; nonnormal random effects; score equation; state space models (search for similar items in EconPapers)
JEL-codes: C13 C61 C63 (search for similar items in EconPapers)
Date: 2003-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:van:wpaper:0319

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