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A unified approach to regression analysis under double‐sampling designs
Author(s) -
Chen YiHau,
Chen Hung
Publication year - 2000
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00243
Subject(s) - estimator , statistics , mathematics , regression analysis , sample (material) , computer science , sampling (signal processing) , parametric statistics , data mining , chemistry , filter (signal processing) , chromatography , computer vision
We propose a unified approach to the estimation of regression parameters under double‐sampling designs, in which a primary sample consisting of data on the rough or proxy measures for the response and/or explanatory variables as well as a validation subsample consisting of data on the exact measurements are available. We assume that the validation sample is a simple random subsample from the primary sample. Our proposal utilizes a specific parametric model to extract the partial information contained in the primary sample. The resulting estimator is consistent even if such a model is misspecified, and it achieves higher asymptotic efficiency than the estimator based only on the validation data. Specific cases are discussed to illustrate the application of the estimator proposed.