TY - JOUR

T1 - Maximization by parts in likelihood inference

AU - Song, Peter X.K.

AU - Fan, Yanqin

AU - Kalbfleisch, John D.

AU - Jiang, Jiming

AU - Louis, Thomas A.

AU - Liao, J. G.

AU - Qaqish, Bahjat F.

AU - Ruppert, David

PY - 2005/12

Y1 - 2005/12

N2 - This article 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 part. Convergence properties of this iterative (fixed-point) algorithm are examined, and asymptotics are derived for estimators obtained using only a finite number of iterations. Illustrative examples considered in the article include multivariate Gaussian copula models, nonnormal random-effects models, generalized linear mixed models, 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.

AB - This article 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 part. Convergence properties of this iterative (fixed-point) algorithm are examined, and asymptotics are derived for estimators obtained using only a finite number of iterations. Illustrative examples considered in the article include multivariate Gaussian copula models, nonnormal random-effects models, generalized linear mixed models, 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.

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U2 - 10.1198/016214505000000204

DO - 10.1198/016214505000000204

M3 - Article

SN - 0162-1459

VL - 100

SP - 1145

EP - 1158

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 472

ER -