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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Archimedean copula model selection under dependent truncation.

D Beaudoin1, L Lakhal-Chaieb

  • 1Département des Opérations et Systèmes de Décision, Université Laval, Pavillon Palasis-Prince, 2325 rue de la terrasse, Québec, Qué., Canada G1V 0A6.

Statistics in Medicine
|June 14, 2008
PubMed
Summary

This study introduces a new method for selecting statistical models for dependent truncation survival data. The approach ranks semi-survival Archimedean copula families using a novel truncated Kendall

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Area of Science:

  • Statistics
  • Survival Analysis
  • Copula Theory

Background:

  • One-sided truncated survival data occur when observations are only available for X < Y.
  • Current analysis methods often assume quasi-independence between time-to-event variables.
  • Semi-survival Archimedean copulas can model dependency in such data.

Purpose of the Study:

  • To develop a model selection procedure for ranking semi-survival Archimedean copula families.
  • To address the challenge of dependent truncation in survival data analysis.
  • To provide a robust method for fitting complex survival data.

Main Methods:

  • Utilizing a truncated version of Kendall's tau for model comparison.
  • Implementing a procedure to rank copula families based on data fit.
  • Applying the method to simulated and real-world datasets.

Main Results:

  • Demonstrated the effectiveness of the proposed model selection procedure.
  • Successfully ranked different semi-survival Archimedean copula families.
  • Validated the approach through simulations and case studies.

Conclusions:

  • The proposed truncated Kendall's tau-based procedure effectively ranks copula families for dependent truncation.
  • This method offers an improved approach for analyzing complex survival data with dependencies.
  • The findings have implications for statistical modeling in various fields dealing with truncated data.