The Indirect Method: Inference Based on Intermediate Statistics—A Synthesis and Examples

Jiang, Wenxin ; Turnbull, Bruce

Statist. Sci., Tome 19 (2004) no. 1, p. 239-263 / Harvested from Project Euclid

Résumé

This article presents an exposition and synthesis of the theory and some applications of the so-called indirect method of inference. These ideas have been exploited in the field of econometrics, but less so in other fields such as biostatistics and epidemiology. In the indirect method, statistical inference is based on an intermediate statistic, which typically follows an asymptotic normal distribution, but is not necessarily a consistent estimator of the parameter of interest. This intermediate statistic can be a naive estimator based on a convenient but misspecified model, a sample moment or a solution to an estimating equation. We review a procedure of indirect inference based on the generalized method of moments, which involves adjusting the naive estimator to be consistent and asymptotically normal. The objective function of this procedure is shown to be interpretable as an “indirect likelihood” based on the intermediate statistic. Many properties of the ordinary likelihood function can be extended to this indirect likelihood. This method is often more convenient computationally than maximum likelihood estimation when handling such model complexities as random effects and measurement error, for example, and it can also serve as a basis for robust inference and model selection, with less stringent assumptions on the data generating mechanism. Many familiar estimation techniques can be viewed as examples of this approach. We describe applications to measurement error, omitted covariates and recurrent events. A dataset concerning prevention of mammary tumors in rats is analyzed using a Poisson regression model with overdispersion. A second dataset from an epidemiological study is analyzed using a logistic regression model with mismeasured covariates. A third dataset of exam scores is used to illustrate robust covariance selection in graphical models.

Détail

Publié le : 2004-05-14
Classification:  Asymptotic normality,  bias correction,  consistency,  efficiency,  estimating equations,  generalized method of moments,  graphical models,  indirect inference,  indirect likelihood,  measurement error,  missing data,  model selection,  naive estimators,  omitted covariates,  overdispersion,  quasi-likelihood,  random effects,  robustness