Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

出版年份 2016 全文链接

标题

作者

关键词

Negative binomial model, Unobserved heterogeneity, Finite-mixture multivariate normal prior, Spatial dependence, Data augmentation, Polya-Gamma random variables, Intrinsic Conditional Auto Regressive (ICAR) priors, Road condition

出版物

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Volume 91, Issue -, Pages 492-510

出版商

Elsevier BV

发表日期

2016-06-26

DOI

10.1016/j.trb.2016.06.005

向作者/读者发起求助以获取更多资源

Protocol

在线求助

Reagent

在线求助

Reprint

联系作者

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Become a Peeref-certified reviewer

The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.

Get Started