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Fitting regression models with response‐biased samples
Author(s) -
Scott Alastair J.,
Wild Chris J.
Publication year - 2011
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.10114
Subject(s) - covariate , statistics , econometrics , regression , parametric statistics , regression analysis , missing data , distribution (mathematics) , mathematics , value (mathematics) , mathematical analysis
This paper extends the work in Lawless, Kalbfleisch, & Wild (1999) on fitting regression models with response‐biased samples, that is, samples where some or all the covariates are missing for some units and the probability that this happens depends in part on the value of the reponse of that unit. In general, the resulting likelihood depends on the distribution of the covariates but we are only interested in methods that do not involve modelling this distribution. We look at a variety of methods based on estimating equations, at the relationship of these methods to semi‐parametric efficient methods in cases where such methods exist, and show ways of obtaining efficiency gains that can sometimes be dramatic. The Canadian Journal of Statistics 39: 519–536; 2011 © 2011 Statistical Society of Canada