Wald Confidence Intervals for a Single Poisson Parameter and Binomial Misclassification Parameter When the Data is Subject to Misclassification

<p>This thesis is based on a Poisson model that uses both error-free data and error-prone data subject to misclassification in the form of false-negative and false-positive counts. We present maximum likelihood estimators (MLEs), Fisher's Information, and Wald statistics for Poisson rate parameter and the two misclassification parameters. Next, we invert the Wald statistics to get asymptotic confidence intervals for Poisson rate parameter and false-negative rate parameter. The coverage and width properties for various sample size and parameter configurations are studied via a simulation study. Finally, we apply the MLEs and confidence intervals to one real data set and another realistic data set.</p>

Name thesis:degree_name

Master of Science - Mathematical Sciences

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mathematics and Statistics

Year dc:date.available

2018

Author dc:creator

• Poddiwala Hewage, Nishantha Janith Chandrasena

Contributors dc:contributor

• Kent Riggs
• Robert K. Henderson
• Jacob Turner

• Wald Statistics
• Fisher's Information
• Poisson Parameter
• Asymptotic Confidence Intervals
• Misclassification Parameter
• Maximum Likelihood Estimators
• Mathematics
• Physical Sciences and Mathematics
• Statistical Models
• Statistical Theory
• Statistics and Probability

Repository record dc:identifier

https://scholarworks.sfasu.edu/etds/202

OAI identifier oai:identifier

oai:scholarworks.sfasu.edu:etds-1217