Methods For Constructing Confidence Intervals For Quantile Regression Coefficients

<p>We describe and compare methods for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the direct relationship between quantile function and probability functions. These might prove practical alternatives to other more popular approaches and can be applied to dependent data, as those that arise in longitudinal, cluster, spatial, and complex survey designs. Results of a simulation study seem to indicate that they may have correct coverage and similar or sometimes narrower confidence intervals than the other methods considered.</p>

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Campus Access Dissertation

Discipline thesis:degree_discipline

Epidemiology and Biostatistics

Year

2011

Author dc:creator

• Wu, Junlong

Contributors dc:contributor

• Matteo Bottai

• Biostatistics
• Physical Sciences and Mathematics
• Statistics and Probability
• bisection method
• confidence interval
• dependent data
• logistic regression
• quantile regression

Statement dc:rights

• © 2011, Junlong Wu

Repository record dc:identifier

https://scholarcommons.sc.edu/etd/560

OAI identifier oai:identifier

oai:scholarcommons.sc.edu:etd-1561