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

To link to this article: https://doi.org/10.1080/24754269.2023.2202579