Abstract/Details

Some classical and Bayesian nonparametric regression methods in a longitudinal marginal model


2004 2004

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Abstract (summary)

We consider nonparametric regression in longitudinal data with dependence within subjects. The object is to estimate the unknown true function at a given point. Both classical and Bayesian approaches are studied.

In classical statistics, the local polynomial kernel method for longitudinal data possesses a somewhat surprising phenomenon called working independence, which implies this common classical method fails to use the correlations inherent in longitudinal data. Motivated by this interesting but somewhat inexplicable phenomenon, we develop a new two-stage kernel method that is carefully designed to utilize the correlation structure.

For Bayesian analysis of longitudinal data, we develop two nonparametric Bayesian regression methods using two prior structures, which are the Dirichlet process mixtures and the Dirichlet Multinomial Allocation mixtures. We compare the performance of the Bayesian methods with the classical method through simulation studies. We also make a suggestion on the choice of hyperpriors and hyperparameters.

Indexing (details)


Subject
Statistics
Classification
0463: Statistics
Identifier / keyword
Pure sciences; Bayesian nonparametric regression; Dirichlet mixtures; Kernel smoothing; Longitudinal marginal; Nonparametric regression
Title
Some classical and Bayesian nonparametric regression methods in a longitudinal marginal model
Author
Seo, Jeonggang
Number of pages
68
Publication year
2004
Degree date
2004
School code
0183
Source
DAI-B 65/11, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780496152902, 0496152904
Advisor
Ghosh, Jayanta K.
University/institution
Purdue University
University location
United States -- Indiana
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3154728
ProQuest document ID
305152182
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305152182/abstract
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