Large Sample Theory of Maximum Likelihood Estimates in Semiparametric Biased Sampling Models

Large Sample Theory of Maximum Likelihood Estimates in Semiparametric Biased Sampling Models

Vardi [Ann. Statist. 13 178-203 (1985)] introduced an s-sample biased sampling model with known selection weight functions, gave a condition under which the common underlying probability distribution G is uniquely estimable and developed simple procedure for computing the nonparametric maximum likel...

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Veröffentlicht in:The Annals of Statistics. - Institute of Mathematical Statistics. - 28(2000), 1, Seite 151-194
1. Verfasser: Gilbert, Peter B. (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2000
Zugriff auf das übergeordnete Werk:The Annals of Statistics
Schlagworte:Asymptotic Theory Choice-Based Sampling Clinical Trials Empirical Processes Generalized Logistic Regression HIV Vaccine Trial Nonparametric Maximum Likelihood Selection Bias Models Vardi's Estimator Philosophy mehr... Mathematics Health sciences
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520 |a Vardi [Ann. Statist. 13 178-203 (1985)] introduced an s-sample biased sampling model with known selection weight functions, gave a condition under which the common underlying probability distribution G is uniquely estimable and developed simple procedure for computing the nonparametric maximum likelihood estimator (NPMLE) Gnof G. Gill, Vardi and Wellner thoroughly described the large sample properties of Vardi's NPMLE, giving results on uniform consistency, convergence of $\sqrt{n}(\mathbb{G}_n - G)$ to a Gaussian process and asymptotic efficiency of Gn. Gilbert, Lele and Vardi considered the class of semiparametric s-sample biased sampling models formed by allowing the weight functions to depend on an unknown finite-dimensional parameter θ. They extended Vardi's estimation approach by developing a simple two-step estimation procedure in which θ̂nis obtained by maximizing a profile partial likelihood and $\mathbb{G}_n \equiv \mathbb{G}_n(\hat{\theta}_n)$ is obtained by evaluating Vardi's NPMLE at θ̂n. Here we examine the large sample behavior of the resulting joint MLE (θ̂n, Gn), characterizing conditions on the selection weight functions and data in order that (θ̂n, Gn) is uniformly consistent, asymptotically Gaussian and efficient. Examples illustrated here include clinical trials (especially HIV vaccine efficacy trials), choice-based sampling in econometrics and case-control studies in biostatistics. 
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650 4 |a Health sciences  |x Medical treatment  |x Biological therapy  |x Immunotherapy  |x Immunization  |x Vaccination 
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