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A Bayesian nonparametric approach to multilevel regression
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posted on 2015-01-01, 00:00 authored by Tien Vu Nguyen, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, H H BuiRegression 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
History
Event
Pacific-Asia Conference on Knowledge Discovery and Data Mining (19th : 2015 : Ho Chi Minh City, Vietnam)Title of book
Advances in knowledge discovery and data miningVolume
9077Series
Lecture Notes in Computer Science; v.9077Chapter number
28Pagination
330 - 342Publisher
SpringerLocation
Ho Chi Minh City, VietnamPlace of publication
Berlin, GermanyPublisher DOI
Start date
2015-01-01End date
2015-01-01ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319180380Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2015, SpringerExtent
58Editor/Contributor(s)
T Cao, E Lim, Z Zhou, T Ho, D Cheung, H MotodaUsage metrics
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