JSM 2005 Online Program

Abstract #303634

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	111
Type:	Invited
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	JASA, Theory and Methods
Abstract - #303634
Title:	Semilinear High-dimensional Models for Normalization of Microarray Data: Theoretical Analysis and Partial Consistency
Author(s):	Jianqing Fan*+ and Heng Peng and Tao Huang
Companies:	Princeton University and Princeton University and Yale University
Address:	Department of Oper. Res. and Fin. Eigineering, Princeton, NJ 08540,
Keywords:
Abstract:	Normalization of microarray data is essential for removing experimental biases and revealing meaningful biological results. Motivated by a problem of normalizing microarray data, a Semilinear In-slide Model (SLIM) was proposed in Fan et al. (2004). To aggregate information from other arrays, SLIM is generalized to account for across-array information, resulting in an even more dynamic semiparametric regression model. This model can be used to normalize microarray data even when there is no replication within an array. We demonstrate that this semiparametric model has a number of interesting features: the parametric component and the nonparametric component that are of primary interest can be consistently estimated, the former possessing parametric rate and the latter having nonparametric rate, while the nuisance parameters can not be consistently estimated. This is an interesting extension of the partial consistent phenomena observed by Neyman and Scott (1948), which is of theoretical interest. We establish the asymptotic normality for the parametric component and the rate of convergence for the nonparametric component.

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