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A Bayesian nonparametric approach to multilevel regression

Nguyen, Vu, Phung, Dinh, Venkatesh, Svetha and Bui, Hung H. 2015, A Bayesian nonparametric approach to multilevel regression. In Cao, Tru, Lim, Ee-Peng, Zhou, Zhi-Hua, Ho, Tu-Bao, Cheung, David and Motoda, Hiroshi (ed), Advances in knowledge discovery and data mining, Springer, Berlin, Germany, pp.330-342, doi: 10.1007/978-3-319-18038-0_26.

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Title A Bayesian nonparametric approach to multilevel regression
Author(s) Nguyen, Vu
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Bui, Hung H.
Title of book Advances in knowledge discovery and data mining
Editor(s) Cao, Tru
Lim, Ee-Peng
Zhou, Zhi-Hua
Ho, Tu-Bao
Cheung, David
Motoda, Hiroshi
Publication date 2015
Series Lecture Notes in Computer Science; v.9077
Chapter number 26
Total chapters 58
Start page 330
End page 342
Total pages 13
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
DIRICHLET PROCESSES
MIXTURES
Summary Regression is at the cornerstone of statistical analysis. Multilevel regression, on the other hand, receives little research attention, though it is prevalent in economics, biostatistics and healthcare to name a few. We present a Bayesian nonparametric framework for multilevel regression where individuals including observations and outcomes are organized into groups. Furthermore, our approach exploits additional group-specific context observations, we use Dirichlet Process with product-space base measure in a nested structure to model group-level context distribution and the regression distribution to accommodate the multilevel structure of the data. The proposed model simultaneously partitions groups into cluster and perform regression. We provide collapsed Gibbs sampler for posterior inference. We perform extensive experiments on econometric panel data and healthcare longitudinal data to demonstrate the effectiveness of the proposed model
ISBN 9783319180380
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-18038-0_26
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076877

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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