This paper is about estimating a random coefficients logit model in which the distribution of each coefficient characterized by finitely many parameters, some may be zero. The gives conditions under which, with probability approaching 1 as sample size increases, penalized maximum likelihood (PML) estimation adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters...

There is a known connection between the multinomial and the Poisson likelihoods. This, in turn, means that a Poisson regression may be transformed into a logit model and vice versa. In this paper, I show the data transformations required to implement this transformation. Several examples are used as illustrations.

Discrete choice models, like the multinomial logit (MNL), have long been recognized for their ability to capture a wide array of transport-related choice phenomena. However, a number of choices are continuous response variables (e.g., location choice, departure time choice, activity duration, and vehicle usage). In this paper, the continuous cross-nested logit (CCNL) model is introduced. The CC...

We introduce Random Multinomial Logit (RML), a general multi-class classifier based on an ensemble of multinomial logistic regression models, and apply it to the task of semantic image segmentation. The algorithm is simple, can be trained efficiently, and has near realtime runtime performance. RML combines the desirable properties of multinomial logistic regression, being stable and theoretical...