The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models
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.
Language
- English
Project
- Status: Completed
- Funding: $20000
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Contract Numbers:
DTRT13-G-UTC58
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Project Managers:
Bhat, Chandra
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Performing Organizations:
Data-Supported Transportation Operations and Planning Center
University of Texas at Austin
Austin, TX United States 78701 -
Principal Investigators:
Bhat, Chandra
- Start Date: 20130930
- Expected Completion Date: 20140930
- Actual Completion Date: 20140930
- Source Data: 101
Subject/Index Terms
- TRT Terms: Choice models; Data analysis; Econometrics; Maximum likelihood method; Spatial analysis
- Subject Areas: Economics; Planning and Forecasting; Transportation (General);
Filing Info
- Accession Number: 01577891
- Record Type: Research project
- Source Agency: Data-Supported Transportation Operations and Planning Center
- Contract Numbers: DTRT13-G-UTC58
- Files: UTC, RiP
- Created Date: Oct 6 2015 4:03PM