JSM 2005 Online Program

Abstract #302350

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.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	1
Type:	Invited
Date/Time:	Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Biopharmaceutical Section
Abstract - #302350
Title:	Towards Automating RJ: A Hierarchical Mixture Model for Protein Spectra
Author(s):	Peter Mueller*+ and Kim-Anh Do and Raj Banyopadhayay and Keith Baggerly
Companies:	The University of Texas M. D. Anderson Cancer Center and The University of Texas M. D. Anderson Cancer Center and Rice University and The University of Texas M. D. Anderson Cancer Center
Address:	1515 Holcombe Boulevard, Box 447, Houston, TX, 77030,
Keywords:	mass/charge spectra ; bayes ; mixture model ; reversible jump
Abstract:	Reversible jump Markov chain Monte Carlo (RJMCMC) and the related pseudo prior approach are conceptually straightforward. However, practical implementation is fraught with difficulties related to the choice of suitable proposals, housekeeping, and careful accounting of all elements of the proposal in the evaluation of the appropriate acceptance probabilities. We develop a framework that allows automation of RJ for location scale mixture models. The final aim is an R function that allows the use of RJ steps in an MCMC implementation, supplying only the likelihood function, a reference solution, and the desired type of move. We will discuss the proposed approach in the context of a hierarchical Bayesian model for MALDI-TOF protein spectra. The sampling distribution will take the form of a mixture of beta kernels, with each kernel representing a distinct protein in the original probe. The prior probability model includes indicators for equal weights, formalizing inference about differential expression of proteins as inference on these latent indicators.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program