Large sample theory for semiparametric regression models with two-phase, outcome dependent sampling
نویسندگان
چکیده
منابع مشابه
Large Sample Theory for Semiparametric Regression Models with Two-phase, Outcome Dependent Sampling By
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 (...
متن کاملLarge Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling
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 ...
متن کاملSemiparametric Inference for Data with a Continuous Outcome from a Two-Phase Probability Dependent Sampling Scheme.
Multi-phased designs and biased sampling designs are two of the well recognized approaches to enhance study efficiency. In this paper, we propose a new and cost-effective sampling design, the two-phase probability dependent sampling design (PDS), for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects...
متن کاملConsistency of Semiparametric Maximum Likelihood Estimators for Two-Phase, Outcome Dependent Sampling
Semiparametric maximum likelihood estimators have recently been proposed for a class of two-phase, outcome dependent sampling models; e.g. Breslow and Holubkov (1997), Scott and Wild (1998), and Lawless, Wild, and Kalb eisch (1999). The estimators studied by these authors are predicated on the estimates of the underlying covariate distribution being concentrated on the observed covariate values...
متن کاملEstimating Variance of the Sample Mean in Two-phase Sampling with Unit Non-response Effect
In sample surveys, we always deal with two types of errors: Sampling error and non-sampling error. One of the most common non-sampling errors is nonresponse. This error happens when some sample units are not observed or viewed but they do not answer some of the questions. The complete prevention of this error is not possible, but it can be significantly reduced. The non-response causes bias and ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2003
ISSN: 0090-5364
DOI: 10.1214/aos/1059655907