Optimal Sampling for Generalized Linear Models Under Measurement Constraints. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Zhang, Tao; Ning, Yang; Ruppert, David
• Is Part Of: Journal of computational and graphical statistics. Volume 30:Issue 1(2021)
• Page Start: 106
• Page End: 114
• Publication Date: 2021-01-02
• Subjects: A-optimality -- Generalized linear models -- Martingale central limit theorem -- Measurement constraints -- Unconditional asymptotic distribution; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2020.1778483
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: Under "measurement constraints, " responses are expensive to measure and initially unavailable on most of records in the dataset, but the covariates are available for the entire dataset. Our goal is to sample a relatively small portion of the dataset where the expensive responses will be measured and the resultant sampling estimator is statistically efficient. Measurement constraints require the sampling probabilities can only depend on a very small set of the responses. A sampling procedure that uses responses at most only on a small pilot sample will be called "response-free." We propose a response-free sampling procedure optimal sampling under measurement constraints (OSUMC) for generalized linear models. Using the A-optimality criterion, that is, the trace of the asymptotic variance, the resultant estimator is statistically efficient within a class of sampling estimators. We establish the unconditional asymptotic distribution of a general class of response-free sampling estimators. This result is novel compared with the existing conditional results obtained by conditioning on both covariates and responses. Under our unconditional framework, the subsamples are no longer independent and new martingale techniques are developed for our asymptotic theory. We further derive the A-optimal response-free sampling distribution. Since this distribution depends on population level quantities, we propose the OSUMC algorithm to approximate the theoretical optimal sampling. Finally, we conduct an intensive empirical study to demonstrate the advantages of OSUMC algorithm over existing methods in both statistical and computational perspectives. We find that OSUMC's performance is comparable to that of sampling algorithms that use complete responses. This shows that, provided an efficient algorithm such as OSUMC is used, there is little or no loss in accuracy due to the unavailability of responses because of measurement constraints. Supplementary materials for this article are available online.