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020 ▼a 9780438122178
035 ▼a (MiAaPQ)AAI10902849
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 004
1001 ▼a S. K., Minhazul Islam.
24510 ▼a Automatic Discovery of Latent Clusters in General Regression Models.
260 ▼a [S.l.] : ▼b University of Florida., ▼c 2017
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2017
300 ▼a 108 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 ▼a Adviser: Arunava Banerjee.
5021 ▼a Thesis (Ph.D.)--University of Florida, 2017.
520 ▼a We present a flexible nonparametric Bayesian framework for automatic detection of local clusters in general regression models. The models are built using techniques that are now considered standard in statistical parameter estimation literature,
520 ▼a In the first part of this thesis, we formulate all traditional versions of the infinite mixture of GLM models under the Dirichlet Process framework. We study extensively two different inference techniques for these models, namely, variational in
520 ▼a In the second part, we present a flexible nonparametric generative model for multigroup regression that detects latent common clusters of groups. We name this "Infinite MultiGroup Generalized Linear Model" (iMG-GLM). We present two versions of t
520 ▼a In the third part, we present a flexible nonparametric generative model for multilevel regression that strikes an automatic balance between identifying common effects across groups while respecting their idiosyncrasies. We name it "Infinite Mixt
520 ▼a For the final problem we present a framework that shows how infinite mixtures of Linear Regression (Dirichlet Process mixtures) can be used to design a new denoising technique in the domain of time series data that presumes a model for the uncor
590 ▼a School code: 0070.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a University of Florida.
7730 ▼t Dissertation Abstracts International ▼g 79-11B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0070
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000415 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자