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  3. Efficient And Multiply Robust Risk Estimation Under General Forms Of Dataset Shift.
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  3. Efficient And Multiply Robust Risk Estimation Under General Forms Of Dataset Shift.

Related Experiment Video

An R-Based Landscape Validation of a Competing Risk Model
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EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Hongxiang Qiu1, Eric Tchetgen Tchetgen2, Edgar Dobriban2

  • 1Department of Epidemiology and Biostatistics, Michigan State University.

Annals of Statistics
|April 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study develops efficient methods for estimating target population risk using auxiliary data, even with dataset shift. These techniques improve machine learning accuracy by leveraging domain adaptation and transfer learning strategies.

Keywords:
62G2068Q32Dataset shiftdomain adaptationefficiencymultiple robustnesssemiparametric modeltransfer learning

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An R-Based Landscape Validation of a Competing Risk Model
05:37

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Area of Science:

  • Statistical Machine Learning
  • Data Science
  • Causal Inference

Background:

  • Machine learning models often suffer from limited target population data.
  • Auxiliary data from related populations can mitigate this data scarcity.
  • Existing domain adaptation and transfer learning methods have limitations in efficient risk evaluation.

Purpose of the Study:

  • To develop efficient estimators for target population risk under various dataset shift conditions.
  • To address the challenge of limited data in statistical machine learning.
  • To improve the accuracy of risk evaluation in target domains using auxiliary data.

Main Methods:

  • Leveraging semiparametric efficiency theory for risk estimation.
  • Developing efficient and multiply robust estimators.
  • Considering a general class of dataset shift conditions, including covariate, label, and concept shift.
  • Allowing for partially nonoverlapping support between source and target populations.

    • Main Results:

    • Efficient estimators for target population risk were developed.
    • A straightforward specification test for dataset shift conditions was created.
    • Efficiency bounds were derived for posterior drift and location-scale shift.
    • Simulation studies confirmed efficiency gains from utilizing dataset shift conditions.

    Conclusions:

    • The proposed methods offer significant efficiency gains for risk estimation under dataset shift.
    • The developed techniques enhance the utility of auxiliary data in machine learning.
    • This work provides a robust framework for addressing data scarcity and domain adaptation challenges.