Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models
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 functio...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2015
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/soe_research/1481 https://ink.library.smu.edu.sg/context/soe_research/article/2480/viewcontent/101007_2F978_3_319_22404_6_21.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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