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Empirical Likelihood based Inference for Additive Partial Linear Measurement Error Models.

Hua Liang1, Haiyan Su, Sally W Thurston

  • 1Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York 14642, U.S.A hliang@bst.rochester.edu.

Statistics and Its Interface
|February 18, 2010
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Summary
This summary is machine-generated.

This study introduces an empirical likelihood method for statistical inference in additive partial linear models with measurement error. The new method improves confidence interval accuracy for analyzing environmental exposure effects on semen quality.

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Area of Science:

  • Statistics
  • Environmental Health
  • Biostatistics

Background:

  • Additive partial linear models are frequently used in statistical analysis.
  • Covariates measured with error can lead to biased inference.
  • Accurate statistical inference is crucial for environmental health studies.

Purpose of the Study:

  • To develop a robust statistical method for additive partial linear models with covariates measured with error.
  • To improve the accuracy of confidence intervals compared to traditional methods.
  • To apply the developed method to analyze real-world environmental exposure data.

Main Methods:

  • Development of an empirical likelihood based statistic.
  • Asymptotic analysis showing the statistic follows a chi-square distribution.
  • Simulation experiments to evaluate finite-sample performance.

Main Results:

  • The proposed empirical likelihood method provides asymptotically chi-square distributed statistics.
  • Simulation studies demonstrate improved accuracy of confidence intervals.
  • The method was successfully applied to analyze semen quality and phthalate exposure.

Conclusions:

  • The empirical likelihood approach offers a valuable tool for statistical inference in the presence of measurement error in additive partial linear models.
  • This method enhances the reliability of findings in environmental health research.
  • The study highlights a significant relationship between phthalate exposure and semen quality.