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...

The availability of longitudinal data allows researchers to analyse the dynamics poverty. By using Turkish Statistical Institute’s (TurkStat) Income and Living Conditions Survey micro dataset, we households’ long-term monetary poverty conditions. We categorise as transitory chronic employ multinomial logit method determinants each types Results indicate that education household size are most ef...

As datasets capturing human choices grow in richness and scale—particularly in online domains—there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce’s choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not ...

The assortment optimization problem under the mixture of multinomial logit models is NPcomplete and there are different approximation methods to obtain upper bounds on the optimal expected revenue. In this paper, we analytically compare the upper bounds obtained by the different approximation methods. We propose a new, tractable approach to construct an upper bound on the optimal expected reven...

This article introduces a Markov chain Monte Carlo (MCMC) method for sampling the parameters of a multinomial logit model from their posterior distribution. Let yi ∈ {0, . . . ,M} denote the categorical response of subject i with covariates xi = (xi1, . . . , xip) T . Let X = (x1, . . . ,xn) T denote the design matrix, and let y = (y1, . . . , yn) T . Multinomial logit models relate yi to xi th...