A modified multinomial baseline logit model with logit functions having different covariates

The multinomial logistic regression is a useful tool in the health and life sciences. In this paper, we propose a modified multinomial baseline logit model for nominal polychotomous data. The modified model is suitable for use in the situation where separate logistic models may be functions of different covariates. An estimation procedure is presented. The modified model is an alternative to the multinomial baseline logit model and the multivariate sparse group lasso. Simulation shows that this modified model outperforms the multinomial baseline logit model and the multinomial sparse group lasso. A real data set about an adolescent placement study is analyzed to demonstrate flexibility and efficiency of the modified model.