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Semiparametric Methods for Response-Selective and Missing Data Problems in Regression

J. F. Lawless, J. D. Kalbfleisch and C. J. Wild
Journal of the Royal Statistical Society. Series B (Statistical Methodology)
Vol. 61, No. 2 (1999), pp. 413-438
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2680650
Page Count: 26
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Semiparametric Methods for Response-Selective and Missing Data Problems in Regression
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Abstract

Suppose that data are generated according to the model f(y∣ x; θ) g(x), where y is a response and x are covariates. We derive and compare semiparametric likelihood and pseudo-likelihood methods for estimating θ for situations in which units generated are not fully observed and in which it is impossible or undesirable to model the covariate distribution. The probability that a unit is fully observed may depend on y, and there may be a subset of covariates which is observed only for a subsample of individuals. Our key assumptions are that the probability that a unit has missing data depends only on which of a finite number of strata that (y, x) belongs to and that the stratum membership is observed for every unit. Applications include case-control studies in epidemiology, field reliability studies and broad classes of missing data and measurement error problems. Our results make fully efficient estimation of θ feasible, and they generalize and provide insight into a variety of methods that have been proposed for specific problems.

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