Weighted scores method for regression models with dependent data

Aristidis K. Nikoloulopoulos, Harry Joe, N. Rao Chaganty

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

There are copula-based statistical models in the literature for regression with dependent data such as clustered and longitudinal overdispersed counts, for which parameter estimation and inference are straightforward. For situations where the main interest is in the regression and other univariate parameters and not the dependence, we propose a “weighted scores method”, which is based on weighting score functions of the univariate margins. The weight matrices are obtained initially fitting a discretized multivariate normal distribution, which admits a wide range of dependence. The general methodology is applied to negative binomial regression models. Asymptotic and small-sample efficiency calculations show that our method is robust and nearly as efficient as maximum likelihood for fully specified copula models. An illustrative example is given to show the use of our weighted scores method to analyze utilization of health care based on family characteristics.
Original languageEnglish
Pages (from-to)653-665
Number of pages13
JournalBiostatistics
Volume12
Issue number4
DOIs
Publication statusPublished - Oct 2011

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