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Updated: Feb 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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ベイジアン機械学習を用いた2段階設計における調査推論の改善

Xinru Wang1,2, Anyu Zhu1, Lauren Kennedy3

  • 1Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|February 16, 2026
PubMed
まとめ

この研究では,公衆衛生調査分析を改善するために,ベイジアンツリーベースの複数の帰算 (MI) 方法を導入しています. この新しいアプローチは,従来の加重方法と比較して,より安定的かつ正確な推定値を提供します.

キーワード:
ベイジアン加法回帰ツリー (BART)デザインの特徴 デザインの特徴高次元の補助変数である.マルチプル・インプテーション2段階の設計です.ウェイトアップは重み付け.

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