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Definition 1.4.1. The sensiticity curve of the estimator Tn based on sample X = {x1, · · · , xn−1} is SC(z; Tn) = n(Tn({x1, · · · , xn−1, z}) − Tn−1({x1, · · · , xn−1})). (1.4) The sensitivity curve is tied to a particular sample. The influence function (or influence curve), introduced by Hampel in year 1974 [3], extended the robustness measure from sample to distribution. Let X has probability distribution P", where # is a parameter we are interested. F(X, #) is the distribution function of P" on Rd. We can express # as a functional T(F) of the distribution function. The estimator of # based on the empirical probability distribution Fn is Tn(Fn). Definition 1.4.2. The influence function (IF) of an estimator T at distribution F is: IF(z; T, F) = lim ##0 T(G#) − T(F) $ = lim ##0 T((1 − $)F + $H) − T(F) $ , where G# is a mixture distribution defined by: G# = (1 − $)F + $H, 0 < $< 1 and H is perturbation distribution. H could be the probability distribution having pointmass one on z, where z is an additional observation; G# is the mixture distribution from which an observation has probability (1−$) of being generated by F and a probability $ of being an arbitrary value z. 7
Object Description
Title  Analysis of robustness and residuals in the Affymetrix gene expression microarray summarization 
Author  Ge, Huanying 
Author email  hge@usc.edu 
Degree  Master of Science 
Document type  Thesis 
Degree program  Statistics 
School  College of Letters, Arts and Sciences 
Date defended/completed  20080701 
Date submitted  2008 
Restricted until  Restricted until 19 June 2010. 
Date published  20100619 
Advisor (committee chair)  Li, Lei M. 
Advisor (committee member) 
Goldstein, Larry M. Chen, Liang 
Abstract  DNA microarray has been widely used in the field of functional genomics. The estimation of gene expression from microarray is a statistical problem where a lot of effort has been made. In this study, we focus on the summarization step of Affymetrix microarray preprocessing. We apply the Least Absolute Deviation (LAD) regression to estimate the probe and treatmentspecific effect in the widelytaken twofactor model. The median polish can be used as an approximation approach for the LAD regression in this twofactor summarization model. We show that the LAD estimator is robust in the sense that it has bounded influence where the bound is strongly associated with the RNA concentration. Furthermore, we calculate the influence bound and standard error which are used as the measure of accuracy for the logratio estimate. 
Keyword  LAD; microarray; robustness 
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  uscthesesm1278 
Contributing entity  University of Southern California 
Rights  Ge, Huanying 
Repository name  Libraries, University of Southern California 
Repository address  Los Angeles, California 
Repository email  cisadmin@lib.usc.edu 
Filename  etdGe20080619 
Archival file  uscthesesreloadpub_Volume32/etdGe20080619.pdf 
Description
Title  Page 15 
Contributing entity  University of Southern California 
Repository email  cisadmin@lib.usc.edu 
Full text  Definition 1.4.1. The sensiticity curve of the estimator Tn based on sample X = {x1, · · · , xn−1} is SC(z; Tn) = n(Tn({x1, · · · , xn−1, z}) − Tn−1({x1, · · · , xn−1})). (1.4) The sensitivity curve is tied to a particular sample. The influence function (or influence curve), introduced by Hampel in year 1974 [3], extended the robustness measure from sample to distribution. Let X has probability distribution P", where # is a parameter we are interested. F(X, #) is the distribution function of P" on Rd. We can express # as a functional T(F) of the distribution function. The estimator of # based on the empirical probability distribution Fn is Tn(Fn). Definition 1.4.2. The influence function (IF) of an estimator T at distribution F is: IF(z; T, F) = lim ##0 T(G#) − T(F) $ = lim ##0 T((1 − $)F + $H) − T(F) $ , where G# is a mixture distribution defined by: G# = (1 − $)F + $H, 0 < $< 1 and H is perturbation distribution. H could be the probability distribution having pointmass one on z, where z is an additional observation; G# is the mixture distribution from which an observation has probability (1−$) of being generated by F and a probability $ of being an arbitrary value z. 7 