Simultaneous variable selection for heteroscedastic regression models

摘要

The simultaneous variable selection for mean model and variance model in heteroscedastie linear models is discussed in this paper. We propose a criterion named PIC based on the adjusted profile log-likelihood function, which can be employed to jointly select regression variables in the mean model and variance model. The proposed criterion is compared with the naive AIC and BIC through a Monte Carlo simulation, and it is shown that PIC outperforms A/C, and is comparable with BIC. In addition, when the sample size is not large, it performs the best,