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Statistics > Methodology

arXiv:1911.00204 (stat)
[Submitted on 1 Nov 2019 (v1), last revised 26 Sep 2022 (this version, v3)]

Title:A regression approach to the two-dataset problem

Authors:Steven N. MacEachern, Koji Miyawaki
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Abstract:This paper considers the two-dataset problem, where data are collected from two potentially different populations sharing common aspects. This problem arises when data are collected by two different types of researchers or from two different sources. We may reach invalid conclusions without using knowledge about the data collection process. To address this problem, this paper develops statistical regression models focusing on the difference in measurement and proposes two prediction errors that help to evaluate the underlying data collection process. As a consequence, it is possible to discuss the heterogeneity/similarity of the set of predictors in terms of prediction. Two real datasets are selected to illustrate our method.
Comments: The final version will be published in Statistics
Subjects: Methodology (stat.ME)
Cite as: arXiv:1911.00204 [stat.ME]
  (or arXiv:1911.00204v3 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.1911.00204

Submission history

From: Koji Miyawaki [view email]
[v1] Fri, 1 Nov 2019 05:25:33 UTC (111 KB)
[v2] Fri, 7 Aug 2020 01:00:39 UTC (162 KB)
[v3] Mon, 26 Sep 2022 00:36:37 UTC (151 KB)
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