JSM 2005 Online Program

Abstract #304129

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:	325
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistical Computing
Abstract - #304129
Title:	A Computationally Quick Bootstrap Procedure for Semiparametric Models
Author(s):	John R. Dixon*+
Companies:	Florida State University
Address:	214 Oceanography and Statistics Building, Tallahassee, FL, 32306, United States
Keywords:	Bootstrap ; Semiparametric Inference ; Empirical Processes ; MCMC ; Biased Sampling ; Survival Analysis
Abstract:	We introduce a computationally quick bootstrap procedure. Like the weighted bootstrap, our procedure can be used to generate random draws that approximate the joint sampling distribution of the parametric and nonparametric maximum likelihood estimators in a variety of semiparametric models, including several useful biased sampling and survival analysis models. But the dimension of the maximization problem for each bootstrapped likelihood is smaller. The procedure can be stated simply. First, obtain a valid random draw for the parametric component of the model, which in many cases can be done at low computational cost. Then, take the draw for the nonparametric component to be the maximizer of the weighted bootstrap profile likelihood with the parametric component fixed at the parametric draw. This avoids the computationally costly computation of the parametric maximizers of the weighted bootstrap likelihoods necessary to give the parametric draws in the weighted bootstrap. We illustrate the computational savings for simulated vaccine efficacy trials.

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