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September 10, 2025Biometrical JournalOpen Access

Unified Estimation Method for Partially Linear Models With Nonmonotone Missing at Random Data

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Authors

YZYang Zhao

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Overview

This research develops a novel estimation technique for partially linear models with missing at random data, improving inference accuracy.

Key Points

  • The proposed method effectively estimates partially linear models even when data is missing at random.
  • Consistent estimators are achieved without depending on the correctness of the working models used.
  • Bootstrap estimates provide robust asymptotic variances for improved estimation efficiency.
  • Simulation studies validate the performance of the new estimation method, highlighting its computational simplicity.

Cite This Study

Yang Zhao (2025) studied this question.

synapsesocial.com/papers/68c1d5e554b1d3bfb60f8967https://doi.org/10.1002/bimj.70070
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