Trial-offer markets, where customers can sample a product before deciding whether to buy it, are ubiquitous in the online experience. Their static and dynamic properties are often studied by assuming that consumers follow a multinomial logit model and try exactly one product. In this paper, we study how to generalize existing results to a more realistic setting where consumers can try multiple ...

it is widely agreed that small scale enterprises (sses) used to play a crucial role in achieving the industrial and economic development. though sses play indispensable economic role, studies are limited to analyze the external factors that affect the growth of sses independently. therefore, the objective of this paper is to examine the effect of external firm factors influencing small scale ma...

 Joint Optimization of Assortment Selection and Pricing under the Capacitated Multinomial Logit Choice Model with Product-Differentiated Price Sensitivities Ruxian Wang HP Laboratories HPL-2012-207 Multinomial Logit model; assortment optimization; multi-product price optimization Many firms face a problem to select an assortment of products and determine their prices to maximize the total prof...

In this research, we propose a novel approach for a multinomial logit model selection procedure: specifically, we apply association rules analysis to identifying potential interactions for multinomial logit modeling. Interaction effects are very common in reality, but conventional multinomial logit model selection methods typically ignore them. This is especially true for higher-order interacti...

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...

3 Model Specification 5 3.1 Binary choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 The binary probit model . . . . . . . . . . . . . . . . . . 6 3.1.2 The binary logit model . . . . . . . . . . . . . . . . . . . 7 3.2 More than two choices . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.1 The multinomial probit model . . . . . . . . . . . . . . . 8 3.2.2 The multinomi...

The so-called “mixed” or “heterogeneous” multinomial logit (MIXL) model has become popular in a number of fields, especially Marketing, Health Economics and Industrial Organization. In most applications of the model, the vector of consumer utility weights on product attributes is assumed to have a multivariate normal (MVN) distribution in the population. Thus, some consumers care more about som...

Gaussian process prior with an appropriate likelihood function is a flexible non-parametric model for a variety of learning tasks. One important and standard task is multi-class classification, which is the categorization of an item into one of several fixed classes. A usual likelihood function for this is the multinomial logistic likelihood function. However, exact inference with this model ha...

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...