Bayesian inference of the cumulative logistic principal component regression models

Minjung Kyung
2022 Communications for Statistical Applications and Methods  
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.
more » ... so, 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.
doi:10.29220/csam.2022.29.2.203 fatcat:kuga7gjewbgpbkzwvpzk5tns6m