This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 52
Type: Invited
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Computing
Abstract - #306094
Title: Automated, Robust Analysis of Quantitative Image Data Using Functional Mixed Models and Isomorphic Basis-Space Modeling
Author(s): Jeffrey S. Morris*+ and Veera Baladandayuthapani and Hongxiao Zhu
Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
Address: PO Box 301402, Houston, TX, 77230-1402, USA
Keywords: functional data analysis ; quantitative image analysis ; isomorphic transforms ; false discovery rate ; robust regression ; proteomics

In this talk, I will describe flexible new automated methods to analyze quantitative image data. The methods are based on functional mixed models, a framework that can simultaneously model multiple factors and account for between-image correlation. We use an isomorphic basis-space approach to fitting the model, which leads to efficient calculations and adaptive smoothing yet flexibly accommodates the complex features characterizing these data. The method is automated and produces inferential plots indicating image regions associated with each factor, simultaneously considering practical and statistical significance, and controlling the false discovery rate. The method can be made robust to accommodate outlying images and handle heavy-tailed errors. It is applied to proteomic data, and is able to find results that would have been missed by conventional analysis approaches.

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