|Tutz, Gerhard and Pößnecker, Wolfgang and Uhlmann, Lorenz (21. June 2012): Variable Selection in General Multinomial Logit Models. Department of Statistics: Technical Reports, No.126|
This is the latest version of this item.
The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate. In this paper we are proposing a sparsity-inducing penalty that accounts for the special structure of multinomial models. In contrast to existing methods, it penalizes the parameters that are linked to one variable in a grouped way and thus yields variable selection instead of parameter selection. We develop a proximal gradient method that is able to efficiently compute stable estimates. In addition, the penalization is extended to the important case of predictors that vary across response categories. We apply our estimator to the modeling of party choice of voters in Germany including voter-specific variables like age and gender but also party-specific features like stance on nuclear energy and immigration.
|Item Type:||Paper (Technical Report)|
|Keywords:||Logistic regression, Multinomial logit model, Variable selection, Lasso, Group Lasso, CATS Lasso.|
|Collections:||Mathematics, Computer Science and Statistics|
Mathematics, Computer Science and Statistics > Statistics
Mathematics, Computer Science and Statistics > Statistics > Technical Reports
|Subjects:||500 Science > 510 Mathematics|
|Deposited On:||01. Oct 2012 23:13|
|Last Modified:||08. Jan 2013 17:09|
Available Versions of this Item
Variable Selection in General Multinomial Logit Models. (deposited 21. Jun 2012 20:40)
- Variable Selection in General Multinomial Logit Models. (deposited 01. Oct 2012 23:13) [Currently Displayed]