Automatic Discovery of Latent Clusters in General Regression Models
General Material Designation
[Thesis]
First Statement of Responsibility
Minhazul Islam S. K.
Subsequent Statement of Responsibility
Banerjee, Arunava
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
University of Florida
Date of Publication, Distribution, etc.
2017
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
108
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Place of publication: United States, Ann Arbor; ISBN=978-0-438-12217-8
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
Ph.D.
Body granting the degree
University of Florida
Text preceding or following the note
2017
SUMMARY OR ABSTRACT
Text of Note
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, namely Dirichlet Process (DP), Hierarchical Dirichlet Process (HDP), Generalized Linear Model (GLM) and Hierarchical Generalized Linear Model (HGLM). These Bayesian nonparametric techniques have been widely applied to solve clustering problems in the real world.