Statistical issues and developments in time series analysis and educational measurement

Chapter 1 is concerned with confidence interval construction for the mean of a long-range dependent time series. It is well known that the moving block bootstrap method produces an inconsistent estimator of the distribution of the normalized sample mean when its limiting distribution is not normal. The subsampling method of Hall, Lahiri and Jing (1998) produces a consistent estimator but involves consistent estimation of the variance of the normalized sample mean using one seemingly arbitrary tuning parameter. By adopting a self-normalization idea, we modify the subsampling procedure of Hall et al.(1998) and the resulting procedure does not require consistent variance estimation. The modi ed subsampling procedure only involves the choice of the subsampling widow width, which can be addressed by using some existing data driven selection methods. Simulations studies are conducted to compare the finite sample performances. The behavior of cluster analysis under different distance measures is explored in Chapter 2, using some of the most common models in educational testing for data generation. Theoretical results on clustering accuracy are given for distance measures used in minimum diameter partitioning and hierarchical agglomerative cluster analysis with complete linkage for data from unidimensional item response models, restricted latent class models for cognitive diagnosis, and the linear factor analysis model. An aim is to identify distance measures that work well for a variety of models, explore how much knowledge of the underlying model is needed to construct a distance measure that leads to a consistent solution, and provide theoretical justi fications for using them. Clustering consistency is defi ned on the space of the latent trait, and consistency and inconsistency results are given for competing distance measures. We study response times in computerized adaptive testing in Chapter 3. We propose a semi-parametric model for response times that arises in educational assessment data. Algorithms for item selection that use the response time information are proposed and studied for their e fficiency and how well they distribute item exposure.

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

• Fan, Zhewen

Contributors dc:contributor

• Douglas, Jeffrey A.
• Shao, Xiaofeng
• Douglas, Jeffrey
• Zhang, Jinming
• Qu, Annie
• Stout, William F.

• Long Range Dependence
• Subsampling
• Modified Subsampling
• Hierarchical Agglomerative Clustering
• Minimum Distance partitioning
• Latent Parameter Models
• Computerized Adaptive Testing
• Response times
• Item Response Theory
• Survival Analysis
• Cox Proportional Model
• Markov Chain Monte Carlo
• Exposure Rate Control.

Statement dc:rights

• Copyright 2010 Zhewen Fan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/18277

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/18277