We consider a sequential assortment selection problem where the user choice is given by multinomial logit (MNL) model whose parameters are unknown. In each period, learning agent observes d-dimensional contextual information about and N available items, offers an of size K to user, bandit feedback item chosen from assortment. propose upper confidence bound based algorithms for this MNL bandit. ...

In this paper, we study optimal model averaging estimators of regression coefficients in a multinomial logit model, which is commonly used many scientific fields. A Kullback–Leibler (KL) loss-based weight choice criterion developed to determine weights. Under some regularity conditions, prove that the resulting are asymptotically optimal. When true one candidate models, averaged consistent. Sim...

A semi-nonparametric generalized multinomial logit model, formulated using orthonormal Legendre polynomials to extend the standard Gumbel distribution, is presented in this paper. The resulting semi-nonparametric function can represent a probability density function for a large family of multimodal distributions. The model has a closed-form log-likelihood function that facilitates model estimat...

Engel curves are often estimated within a linear, or at least approximately linear system of equations. However, Engel curves are required to lie on or within the unit interval, while summing to unity. These restrictions are not easily accommodated within a linear system. Therefore, we apply the fractional multinomial logit model in our estimation of expenditure shares, because it more readily ...

Unique parametrizations of models are very important for parameter interpretation and consistency of estimators. In this paper we analyze the identifiability of a general class of finite mixtures of multinomial logits with varying and fixed effects, which includes the popular multinomial logit and conditional logit models. The application of the general identifiability conditions is demonstrate...