Review Articles

Kernel regression utilizing heterogeneous datasets

Chi-Shian Dai ,

Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA

Jun Shao

Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA;School of Statistics, East China Normal University, Shanghai, People's Republic of China

Pages | Received 05 Dec. 2022, Accepted 08 Apr. 2023, Published online: 28 Apr. 2023,
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Data analysis in modern scientific research and practice has shifted from analysing a single dataset to coupling several datasets. We propose and study a kernel regression method that can handle the challenge of heterogeneous populations. It greatly extends the constrained kernel regression [Dai, C.-S., & Shao, J. (2023). Kernel regression utilizing external information as constraints. Statistica Sinica, 33, in press] that requires a homogeneous population of different datasets. The asymptotic normality of proposed estimators is established under some conditions and simulation results are presented to confirm our theory and to quantify the improvements from datasets with heterogeneous populations.


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To cite this article: Chi-Shian Dai & Jun Shao (2024) Kernel regression utilizing heterogeneous datasets, Statistical Theory and Related Fields, 8:1, 51-68, DOI: 10.1080/24754269.2023.2202579

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