Theory and applications of nonparametric regression in item response theory

The simultaneous and nonparametric estimation of latent abilities and item characteristic curves is considered. In particular, the joint asymptotic properties of ordinal ability estimation and kernel smoothed nonparametric item characteristic curve estimation is investigated under relatively unrestrictive assumptions on the underlying item response theory model as both test length and sample size increase. A large deviation probability inequality is given for ordinal ability estimation. The mean squared error of kernel smoothed item characteristic curve estimates is studied and a strong consistency result is obtained showing that the worst case error in the item characteristic curve estimates over all items and ability levels converges to zero with probability equal to one.

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

2011

Author dc:creator

• Douglas, Jeffrey A.

Contributors dc:contributor

• Stout, William F.

• Statistics
• Psychology, Psychometrics

Statement dc:rights

• Copyright 1995 Douglas, Jeffrey Alan

Language dc:language

eng

Identifier

AAI9624336
(UMI)AAI9624336

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/22148