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SYMMETRIC AND TRIMMED SOLUTIONS OF SIMPLE LINEAR REGRESSION
by
Chao Cheng
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(STATISTICS)
December 2006
Copyright 2006 Chao Cheng
Object Description
| Title | Symmetric and trimmed solutions of simple linear regression |
| Author | Cheng, Chao |
| Author email | chengchao12@hotmail.com |
| Degree | Master of Science |
| Document type | Dissertation |
| Degree program | Statistics |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2006-10-01 |
| Date submitted | 2006 |
| Restricted until | Unrestricted |
| Date published | 2006-10-25 |
| Advisor (committee chair) | Li, Lei |
| Advisor (committee member) |
Zhang, Jianfeng Goldstein, Larry Chen, Liang |
| Abstract | Least trimmed squares (LTS), as a robust method, is widely used in linear regression models. However, the ordinary LTS of simple linear regression treats the response and prediction variable asymmetrically. In other world, it only considers the errors from response variable. This treatment is not appropriate in some applications. To overcome these problems, we develop three versions of symmetric and trimmed solutions that take into consideration errors from both response and predictor variable.In the thesis, we describe the algorithms to achieve the exact solutions for these three symmetric LTS. We show that these methods lead to more sensible solutions than ordinary LTS. We also apply one of the methods to the microarray normalization problem. It turns out that our LTS based normalization method has advantages over other available normalization methods for microarray data set with large differentiation fractions.; In the thesis, we describe the algorithms to achieve the exact solutions for these three symmetric LTS. We show that these methods lead to more sensible solutions than ordinary LTS.We also apply one of the methods to the microarray normalization problem. It turns out that our LTS based normalization method has advantages over other available normalization methods for microarray data set with large differentiation fractions. |
| Keyword | LTS; simple linear regression; microarray |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m108 |
| Rights | Cheng, Chao |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Cheng-20061025 |
| Archival file | uscthesesreloadpub_Volume32/etd-Cheng-20061025.pdf |
Description
| Title | Page 1 |
| Full text | SYMMETRIC AND TRIMMED SOLUTIONS OF SIMPLE LINEAR REGRESSION by Chao Cheng A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (STATISTICS) December 2006 Copyright 2006 Chao Cheng |
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