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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sg-smu-ink.soe_research-24802018-01-18T05:28:01Z Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models Wrobel, T. LEUNG, Denis H. Y. Qin, J. Hettmansperger, T. 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. 2015-01-01T08:00:00Z text application/pdf 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Empirical likelihood Exponential tilting Repeated measures Mixture distribution Multivariate Economics |
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Empirical likelihood Exponential tilting Repeated measures Mixture distribution Multivariate Economics Wrobel, T. LEUNG, Denis H. Y. Qin, J. Hettmansperger, T. Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models |
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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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Wrobel, T. LEUNG, Denis H. Y. Qin, J. Hettmansperger, T. |
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Wrobel, T. LEUNG, Denis H. Y. Qin, J. Hettmansperger, T. |
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Wrobel, T. |
title |
Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models |
title_short |
Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models |
title_full |
Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models |
title_fullStr |
Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models |
title_full_unstemmed |
Semiparametric Analysis in Conditionally Independent Multivariate Mixture Models |
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semiparametric analysis in conditionally independent multivariate mixture models |
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Institutional Knowledge at Singapore Management University |
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2015 |
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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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