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Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Norman Breslow, Brad McNeney and Jon A. Wellner
The Annals of Statistics
Vol. 31, No. 4 (Aug., 2003), pp. 1110-1139
Stable URL: http://www.jstor.org/stable/3448453
Page Count: 30
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling
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Abstract

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part agrees with the more general information bound calculations of Robins, Hsieh and Newey (1995). By verifying the conditions of Murphy and van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

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