.png){kind=icon}
返回
A simple method for estimating preference parameters for individuals
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijresmar.2013.07.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper demonstrates a method for estimating logit choice models for small sample data, including single individuals, that is computationally simpler and relies on weaker prior distributional assumptions compared to hierarchical Bayes estimation. Using Monte Carlo simulations and online discrete choice experiments, we show how this method is particularly well suited to estimating values of choice model parameters from small sample choice data, thus opening this area to the application of choice modeling. For larger sample sizes of approximately 100-200 respondents, preference distribution recovery is similar to hierarchical Bayes estimation of mixed logit models for the examples we demonstrate. We discuss three approaches for specifying the conjugate priors required for the method: specifying priors based on existing or projected market shares of products, specifying a flat prior on the choice alternatives in a discrete choice experiment, or adopting an empirical Bayes approach where the prior choice probabilities are taken to be the average choice probabilities observed in a discrete choice experiment. We show that for small sample data, the relative weighting of the prior during estimation is an important consideration, and we present an automated method for selecting the weight based on a predictive scoring rule. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Choice modeling
Bayesian estimation
Discrete choice experiment
Conjugate prior
Individual level choice model
期刊
IF:
7.5
论文数:
1.2K
被引数:
6.4K
机构
引用论文
.png){kind=icon}
Designs with a priori information for nonmarket valuation with choice experiments:: A Monte Carlo study具有先验信息的设计，用于通过选择实验进行非市场估值:: 蒙特卡洛研究
PREAI
分享收藏
查看更多内容