說明 
135 p 
附註 
Source: Dissertation Abstracts International, Volume: 7104, Section: B, page: 2470 

Adviser: Yinglei Lai 

Thesis (Ph.D.)The George Washington University, 2010 

Microarray technology is extensively used today in many applications. One of its uses is discovering sets of genes that are most likely to be related to cancer. Typically, based on microarray data, a statistical test is conducted for each of thousands of genes simultaneously. In a twogroup comparative microarray experiment, an important parameter for controlling the rate of false positives and also for determining the appropriate sample size is the proportion of true null hypotheses (pi0) 

To obtain an improved estimation method for pi0, we modify an existing simple method by introducing artificial censoring to pvalues. The model is a twocomponent mixture of a censored Uniform(0,1) and a censored Beta(alpha,1) distribution. The model fitting is achieved through the Expectation Maximization algorithm. In a comprehensive simulation study and applications to experimental data sets, we illustrate the benefits of using our method by comparing its performance to other existing methods 

Furthermore, we develop and study the properties of a likelihood ratio test for pi0 at different combinations of sample size, number of genes, and H0 value of pi0. Maximization of the restricted likelihood is achieved by incorporating a linear constraint of the parameters into the Expectation Maximization algorithm. The pvalue of the test is based on a parametric bootstrap approach. We illustrate the usefulness of the test through applications to experimental datasets 

School code: 0075 
Host Item 
Dissertation Abstracts International 7104B

主題 
Biology, Biostatistics


Statistics


0308


0463

Alt Author 
The George Washington University. Statistics

