The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models
| 项目名称： | The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models |
| 摘要： | The composite marginal likelihood (CML) inference approach is a relatively simple approach that can be used when the full likelihood function is practically infeasible to evaluate due to underlying complex dependencies. The history of the approach may be traced back to the pseudo-likelihood approach of Besag (1974) for modeling spatial data, and has found traction in a variety of fields since, including genetics, spatial statistics, longitudinal analyses, and multivariate modeling. However, the CML method has found little coverage in the econometrics field, especially in discrete choice modeling. This project will fill this gap by identifying the value and potential applications of the method in discrete dependent variable modeling as well as mixed discrete and continuous dependent variable model systems. |
| 状态： | Completed |
| 资金： | $20000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Bhat, Chandra R |
| 执行机构： | Data-Supported Transportation Operations and Planning Center |
| 开始时间： | 20130930 |
| 预计完成日期： | 20140930 |
| 实际结束时间： | 20140930 |
| 主题领域： | Economics；Planning and Forecasting；Transportation (General) |
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