A unified approach to regression analysis under double-sampling designs

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2000 · Volume 62 · Issue 3

Abstract

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.

Keywords

Double sampling, Estimating equation, Generalized linear model, Incomplete data, Validation sample

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