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University of Illinois at Urbana-Champaign

Bayesian Inference in Nonparametric Logistic Regression

Abstract

dc:description

We consider the problem of regressing a dichotomous response variable on a predictor variable. Our interest is in modelling the probability of occurrence of the response as a function of the predictor variable, and in inferences about the estimated function. The log-odds (logit) of the probability is estimated nonparametrically, using generalized smoothing splines.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Statistics
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2014

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Raghavan, Nandini
Contributors dc:contributor
  • Cox, Dennis D.

Subjects

dc:subject × 1

Identifiers

dc:identifier.*
Identifier
(UMI)AAI9411757
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/72584

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Raghavan, Nandini. Bayesian Inference in Nonparametric Logistic Regression. Dissertation thesis, University of Illinois at Urbana-Champaign, 2014. http://hdl.handle.net/2142/72584