Evaluation of performance of non-parametric confidence intervals on skewed data

In this thesis we will develop a method for the construction of a non-parametric confidence interval and compare it to parametric confidence intervals for the mean of a population distribution. Using Monte Carlo simulation, we will examine the performance of both confidence intervals on data from different types of distributions. In particular, we are interested in examining how these confidence intervals perform when the data is known to come from a skewed distribution. Our goal is to illustrate the superiority of our new method against a traditional parametric approach for estimating parameters, particularly on skewed data.

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mathematical Sciences

Grantor dc:publisher

University of Nevada, Las Vegas

Year

2003

Author dc:creator

• Solis, Monica

Contributors dc:contributor

• Ashok K. Singh

Statement dc:rights

• IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English

Identifier

https://oasis.library.unlv.edu/rtds/1563

OAI identifier oai:identifier

oai:oasis.library.unlv.edu:rtds-2562