Publication Type
Book Chapter
Version
publishedVersion
Publication Date
1-2015
Abstract
The conditional independence assumption is commonly used in multivariate mixture models in behavioral research. We propose an exponential tilt model to analyze data from a multivariate mixture distribution with conditionally independent components. In this model, the log ratio of the density functions of the components is modeled as a quadratic function in the observations. There are a number of advantages in this approach. First, except for the exponential tilt assumption, the marginal distributions of the observations can be completely arbitrary. Second, unlike some previous methods, which require the multivariate data to be discrete, modeling can be performed based on the original data.
Keywords
Empirical likelihood, Exponential tilting, Repeated measures, Mixture distribution, Multivariate
Discipline
Economics
Research Areas
Econometrics
First Page
371
Last Page
392
ISBN
9783319224039
Publisher
Springer
City or Country
Switzerland
Citation
Wrobel, T.; LEUNG, Denis H. Y.; Qin, J.; and Hettmansperger, T..
Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models. (2015). 371-392.
Available at: https://ink.library.smu.edu.sg/soe_research/1481
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org./10.1007/978-3-319-22404-6_21