A Bayesian sample selection model based on normal mixture to investigate household car ownership and usage behavior. (July 2020)

• Record Type: Journal Article
• Authors: Wu, Na; Song, Xiang (Ben); Yao, Ronghan; Yu, Qian; Tang, Chunyan; Zhao, Shengchuan
• Is Part Of: Travel behaviour and society. Volume 20(2020)
• Page Start: 36
• Page End: 50
• Publication Date: 2020-07
• Subjects: Sample selection -- Normal mixture -- Bayesian Markov Chain Monte Carlo -- Copula model -- Car ownership and usage; Transportation -- Periodicals; Population geography -- Periodicals; 303.48305
• DOI: 10.1016/j.tbs.2020.02.006
• Languages: English
• ISSNs: 2214-367X

Abstract

Highlights: A Bayesian sample selection model based on normal mixture is used to investigate household car ownership and usage behavior. The new model can capture flexible coupling relationships without specifying the marginal distributions. Various tests are proposed and used to examine the model performance. Estimates from the traditional normal model are biased regarding to magnitude, significance level, even the sign. The new model with two normal components can improve the goodness of fit significantly compared with the traditional normal model and the Copula model. Abstract: Selection bias is an important issue in analyzing household car ownership and usage behavior. If it is not well considered in the modeling process, estimates will be biased. In this paper, we use a Bayesian sample selection model, which accounts for the selection bias, to investigate household car ownership and usage behavior. Employing the approach of normal mixture, the new established model relaxes the bivariate normal assumption in the traditional sample selection model and can capture flexible coupling relationship between car ownership and usage behavior. Moreover, the model does not require specifying any marginal distribution. Three cross validation experiments using simulated data suggest that the new model is effective in revealing parameters' true values and in capturing actual error distribution. Considering overfitting issue, various tests are proposed to determine the most likely number of normal components. After testing, the new model with 3 components has a stronger explanation power in analyzing the interdependence between household car ownership and usage behavior in terms of goodness of fit and generalization ability. By comparison, estimates from the traditional normal model are seriously biased regarding magnitude, significance level, even the sign. Lastly, to test the efficiency of Bayesian normal mixture model, performance of the Copula model is evaluated. The result indicates that the normal mixture model with two components already has a strong power in capturing the general pattern of error distribution, and its goodness of fit has been impressively improved compared with the traditional normal model and the Copula model.