Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Bayesian inference of the cumulative logistic principal component regression models

  • Kyung, Minjung (Department of Statistics, Duksung Women's University)
  • Received : 2021.09.04
  • Accepted : 2022.02.21
  • Published : 2022.03.31
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Abstract

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1049834).

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