Incomplete data in generalized linear models with continuous covariates

Sanford Weisberg, J Ibrahim

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This paper proposes a method for estimating the parameters in a generalized linear model with missing covariates. The missing covariates are assumed to come from a continuous distribution, and are assumed to be missing at random. In particular, Gaussian quadrature methods are used on the E‐step of the EM algorithm, leading to an approximate EM algorithm. The parameters are then estimated using the weighted EM procedure given in Ibrahim (1990). This approximate EM procedure leads to approximate maximum likelihood estimates, whose standard errors and asymptotic properties are given. The proposed procedure is illustrated on a data set. Copyright © 1992, Wiley Blackwell. All rights reserved

Original languageEnglish
Pages (from-to)461-470
Number of pages10
JournalAustralian Journal of Statistics
Volume34
Issue number3
DOIs
StatePublished - 1992

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