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Discrete Choice Methods with Simulation
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In this paper, the authors describe the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation, and compare simulation-assisted estimation procedures, including maximum simulated likelihood, method of simulated moments, and methods of simulated scores.Abstract:
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.read more
Citations
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Consumer preferences for electric vehicles in lower tier cities of China: Evidences from south Jiangsu region
Youlin Huang,Lixian Qian +1 more
TL;DR: Zhang et al. as mentioned in this paper investigated consumer preferences for EVs in lower tier cities of China, by collecting stated preference (SP) data in two second-tier and three third-tier cities in the south Jiangsu region of China.
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Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach.
TL;DR: A unique database from the California Department of Motor Vehicles 124 manufacturer-reported Traffic Collision Reports was created and was linked with detailed data on roadway and built-environment attributes, revealing that when the automated driving system is engaged and remains engaged, the likelihood of an AV-involved rear-end crash is substantially higher compared to a conventionally-driven AV.
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On Studying the Impact of Uncertainty on Behavior Diffusion in Social Networks
TL;DR: This paper deeply explores the pattern of gossip diffusion in social networks when uncertainty exists in users' decision making, and inspires by random utility theory to formulate the diffusion model based on mixed logit model that allows for user's uncertainty in determining whether to adopt a specific strategy.
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Cyclists' red-light running behaviours: An examination of risk-taking, opportunistic, and law-obeying behaviours
Chih Wei Pai,Rong-Chang Jou +1 more
TL;DR: This research develops a mixed logit model of bicyclists' three distinct crossing behaviours by classifying crossing behaviours into three distinct manners: risk-taking, opportunistic, and law-obeying, which lends support to the use of mixed logits in bicyclist RLV research.
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Understanding suburban travel demand: Flexible modelling with revealed and stated choice data
TL;DR: In this article, the authors analyse the choice of mode in suburban corridors using nested logit specifications with revealed and stated preference data, which were obtained from a choice experiment between car and bus, which allowed for interactions among the main policy variables: travel cost, travel time and frequency.
References
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Maximum likelihood from incomplete data via the EM algorithm
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Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
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Sample Selection Bias as a Specification Error
TL;DR: In this article, the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias is discussed, and the asymptotic distribution of the estimator is derived.