scholarly journals Gaussian process latent class choice models

2022 ◽  
Vol 136 ◽  
pp. 103552
Author(s):  
Georges Sfeir ◽  
Filipe Rodrigues ◽  
Maya Abou-Zeid
Keyword(s):  
Gaussian Process ◽  
Latent Class ◽  
Choice Models

scholarly journals Análisis econométrico de la heterogeneidad de las preferencias de los individuos: Aplicación a la valoración económica de la conservación del paisaje agrícola de montaña

2011 ◽  
Vol 8 (1) ◽  
pp. 103 ◽  
Author(s):  
Sergio Colombo ◽  
Nick Hanley
Keyword(s):  
Latent Class ◽  
Random Parameter ◽  
Choice Models ◽  
Latent Class Models ◽  
Preference Heterogeneity ◽  
Stated Choice ◽  
Empirical Application ◽  
Random Parameter Logit ◽  
Class Models

The need to account for respondents’ preference heterogeneity in stated choice models has motivated researchers to apply random parameter logit and latent class models. In this paper we compare these three alternative ways of incorporating preference heterogeneity in stated choice models and evaluate how the choice of model affects welfare estimates in a given empirical application. Finally, we discuss what criteria to follow to decide which approach is most appropriate.

scholarly journals The Influence of Alcohol Warning Labels on Consumers’ Choices of Wine and Beer

2020 ◽  
Vol 9 (2) ◽  
pp. 3-21
Author(s):  
Azzurra Annunziata ◽  
Lara Agnoli ◽  
Riccardo Vecchio ◽  
Steve Charters ◽  
Angela Mariani
Keyword(s):  
Alcohol Abuse ◽  
Discrete Choice ◽  
Latent Class ◽  
Choice Models ◽  
Discrete Choice Experiments ◽  
Health Warnings ◽  
Negative Effects ◽  
Excessive Consumption ◽  
Targeted Messages ◽  
Different Levels

This study aims to analyse the influence of alternative formats of health warnings on French and Italian Millennial consumers’ choices of beer and wine. Two Discrete Choice Experiments were built for wine and beer and two Latent Class choice models were applied in order to verify the existence of different consumer profiles. Results show that young consumers’ choices for wine and beer are influenced by framing, design and visibility of warnings. In both countries, the acceptance of warnings is higher for beer than for wine and in both cases consumers show higher utility for a logo on the front label: on the neck with a neutral message in the case of beer; on the front, without a message for wine. Latent Class choice models highlight the existence of different consumers’ groups with different levels of warning influencing their choices. In order to apply policies conducting to health benefits, our results suggest the need to focus on young individuals to communicate the risks of alcohol abuse through targeted messages and, more generally, to make them aware of the potential negative effects of excessive consumption of both wine and beer.

A Comparative Empirical Study of Discrete Choice Models in Retail Operations

2021 ◽  
Author(s):  
Gerardo Berbeglia ◽  
Agustín Garassino ◽  
Gustavo Vulcano
Keyword(s):  
Empirical Study ◽  
Discrete Choice ◽  
Operations Management ◽  
Latent Class ◽  
Markov Chain Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Chain Model ◽  
Retail Operations ◽  
Demand Models

Choice-based demand estimation is a fundamental task in retail operations and revenue management, providing necessary input data for inventory control, assortment, and price-optimization models. The task is particularly difficult in operational contexts where product availability varies over time and customers may substitute into the available options. In addition to the classical multinomial logit (MNL) model and extensions (e.g., nested logit, mixed logit, and latent-class MNL), new demand models have been proposed (e.g., the Markov chain model), and others have been recently revisited (e.g., the rank list-based and exponomial models). At the same time, new computational approaches were developed to ease the estimation function (e.g., column-generation and expectation-maximization (EM) algorithms). In this paper, we conduct a systematic, empirical study of different choice-based demand models and estimation algorithms, including both maximum-likelihood and least-squares criteria. Through an exhaustive set of numerical experiments on synthetic, semisynthetic, and real data, we provide comparative statistics of the predictive power and derived revenue performance of an ample collection of choice models and characterize operational environments suitable for different model/estimation implementations. We also provide a survey of all the discrete choice models evaluated and share all our estimation codes and data sets as part of the online appendix. This paper was accepted by Vishal Gaur, operations management.

Exploring Marketing to Engineering Information Mapping in Mass Customization: A Presentation of Ideas, Challenges and Resulting Questions

2011 ◽  
Author(s):  
Scott Ferguson ◽  
Andrew Olewnik ◽  
Phil Cormier
Keyword(s):  
Discrete Choice ◽  
Mass Customization ◽  
Latent Class ◽  
Mixed Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Hierarchical Bayes ◽  
Customer Preferences ◽  
Lack Of Information ◽  
Engineering Information

The paradigm of mass customization strives to minimize the tradeoffs between an ‘ideal’ product and products that are currently available. However, the lack of information relation mechanisms that connect the domains of marketing, engineering, and distribution have caused significant challenges when designing products for mass customization. For example, the bridge connecting the marketing and engineering domains is complicated by the lack of proven tools and methodologies that allow customer needs and preferences to be understood at a level discrete enough to support true mass customization. Discrete choice models have recently gained significant attention in engineering design literature as a way of expressing customer preferences. This paper explores how information from choice-based conjoint surveys might be used to assist the development of a mass customizable MP3 player, starting from 140 student surveys. The authors investigate the challenges of fielding discrete choice surveys for the purpose of mass customization, and explore how hierarchical Bayes mixed logit and latent class multinomial logit models might be used to understand the market for customizable attributes. The potential of using discrete choice models as a foundation for mathematically formulating mass customization problems is evaluated through an investigation of strengths and limitations.

scholarly journals Integrating psychometric indicators in latent class choice models

2014 ◽  
Vol 64 ◽  
pp. 135-146 ◽  
Author(s):  
Ricardo Hurtubia ◽  
My Hang Nguyen ◽  
Aurélie Glerum ◽  
Michel Bierlaire
Keyword(s):  
Latent Class ◽  
Choice Models

Hurdle and Latent Class Approaches to Serial Non-Participation in Choice Models

2008 ◽  
Vol 42 (2) ◽  
pp. 211-226 ◽  
Author(s):  
Mike Burton ◽  
Dan Rigby
Keyword(s):  
Latent Class ◽  
Choice Models

Nonlinear Effects in Brand Choice Models: Comparing Heterogeneous Latent Class To Homogeneous Nonlinear Models

2007 ◽  
Vol 59 (2) ◽  
pp. 118-137 ◽  
Author(s):  
Marion Schindler ◽  
Bernhard Baumgartner ◽  
Harald Hruschka
Keyword(s):  
Latent Class ◽  
Nonlinear Models ◽  
Brand Choice ◽  
Nonlinear Effects ◽  
Choice Models

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

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