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Linearized maximum rank correlation estimation of doubly truncated data.

Peijie Wang1, Qihao Wang1, Jianguo Sun2

  • 1School of Mathematics, Jilin University, China.

Statistical Methods in Medical Research
|April 2, 2026
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Summary
This summary is machine-generated.

We developed a new statistical method for analyzing incomplete (truncated) data, offering a simpler and more efficient approach for economics and survival analysis. This linearized maximum rank correlation estimation provides accurate results without needing complex assumptions about data distribution.

Keywords:
Close-form solutiondoubly truncated datalinearized maximum rank correlation estimationsingle-index model

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Area of Science:

  • Statistics
  • Econometrics
  • Survival Analysis

Background:

  • Truncated data, common in economics and survival analysis, pose challenges for statistical inference due to missing information.
  • Existing methods for handling truncated data can be computationally intensive and require specific distributional assumptions.

Purpose of the Study:

  • To propose a novel linearized maximum rank correlation estimation for doubly truncated data within a single-index model.
  • To develop an estimation method that is computationally efficient and does not require prior knowledge of the link function or error distribution.

Main Methods:

  • Utilizing linearized maximum rank correlation to estimate parameters for doubly truncated data.
  • Employing a single-index model framework to handle the truncated data structure.

Main Results:

  • The proposed estimation provides a closed-form expression, simplifying computation.
  • The estimators are proven to be consistent and asymptotically normal.
  • Simulation studies demonstrate the method's effectiveness across various scenarios.

Conclusions:

  • The linearized maximum rank correlation estimation offers a theoretically sound and computationally advantageous approach for analyzing doubly truncated data.
  • The method is robust and applicable in diverse fields, including economics, astronomy, and survival analysis, as evidenced by its successful application to an AIDS study.