Subsampling in Longitudinal Models

Ziyang Wang (), HaiYing Wang () and Nalini Ravishanker ()
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Ziyang Wang: University of Connecticut
HaiYing Wang: University of Connecticut
Nalini Ravishanker: University of Connecticut

Methodology and Computing in Applied Probability, 2023, vol. 25, issue 1, 1-29

Abstract: Abstract For large scale data, subsampling methods are often used to approximate the full-data parameter estimates. An ideal subsampling method picks a small proportion of informative observations from the full data and produces an accurate approximate to the full-data estimate using much less computing power. Existing studies on subsampling methods focus on independent responses. This paper discusses subsampling methods for longitudinal data where observations within a block are correlated, and develops optimal subsampling methods to approximate the full-data maximum likelihood estimators of the model parameters. We first establish the conditional asymptotic distribution of the subsample estimator with general subsampling probabilities, and then derive the optimal subsampling method that minimizes the asymptotic mean squared error of the subsample estimator. To evaluate the finite sample performance of the proposed method, we provide results based on numerical experiments with simulated data.

Keywords: Large data; Fisher scoring; Optimal subsampling; 62D05; 62F10; 62k05 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11009-023-10015-4

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