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- Title
Parametric models for response-biased sampling.
- Authors
Chen, Kani
- Abstract
Suppose that subjects in a population follow the model f ( y [sup *] | x [sup ast] ; θ ) where y [sup ast] denotes a response, x [sup ast] denotes a vector of covariates and θ is the parameter to be estimated. We consider response-biased sampling, in which a subject is observed with a probability which is a function of its response. Such response-biased sampling frequently occurs in econometrics, epidemiology and survey sampling. The semiparametric maximum likelihood estimate of θ is derived, along with its asymptotic normality, efficiency and variance estimates. The estimate proposed can be used as a maximum partial likelihood estimate in stratified response-selective sampling. Some computation algorithms are also provided.
- Subjects
PARAMETER estimation; STATISTICAL sampling; MAXIMAL functions; ALGORITHMS; RESPONSE surfaces (Statistics)
- Publication
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2001, Vol 63, Issue 4, p775
- ISSN
1369-7412
- Publication type
Article
- DOI
10.1111/1467-9868.00312