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Parametric robust inferences for correlated ordinal data.

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  • 1Institute of Statistics, National Central University, Taiwan, Province of China. tsou@mx.stat.ncu.edu.tw

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This study introduces a new method for analyzing correlated ordinal data, offering reliable statistical inferences without needing to know the full data distribution. The approach is validated through simulations and real-world data analysis.

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Correlated ordinal response variables are common in various fields.
  • Existing methods often require strong assumptions about data distribution.
  • Accurate statistical inference is crucial for reliable analysis.

Purpose of the Study:

  • To develop asymptotically valid likelihood inferences for regression parameters.
  • To address correlated ordinal response variables.
  • To offer a method robust to unknown joint distributions.

Main Methods:

  • A novel parametric approach for likelihood inference.
  • Utilizes second moments of joint distributions.
  • Validates efficacy through Monte Carlo simulations.

Main Results:

  • The proposed method provides asymptotically valid inferences.
  • Demonstrated robustness to unspecified joint distributions.
  • Successful application to two real-world datasets.

Conclusions:

  • The new method offers a flexible and valid approach for analyzing correlated ordinal data.
  • It reduces the need for restrictive distributional assumptions.
  • The approach is practical and effective as shown by empirical examples.