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

Abstract Details

Activity Number: 37
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #308297
Title: The Gap Bootstrap
Author(s): Clifford Spiegelman*+ and Soumendra Nath Lahiri and Justice Appiah and Laurence Rilett
Companies: Texas A&M University and Texas A&M University and Nebraska Transportation Center and Nebraska Transportation Center
Address: Department of Statistics-3143, College Station, TX, 77845-3143,
Keywords: Bootstrap ; resampling ; transportation ; multivariate
Abstract:

In many areas of application, multivariate data are collected routinely over long time periods. Examples include hydrocarbon pollution monitoring, and automated highway volume traffic monitoring. The dominant part or the dependence for these types of data is short term. The gap bootstrap uses a divide, estimate, assess and combine strategy to provide asymptotically optimal or near optimal estimators. In spirit, it is similar in approach to kernel regression estimation, except that the joined pieces are not contiguous in time. We will show that for smooth enough estimators, and some useful dependence models that the resulting estimators are asymptotically efficient and have uncertainties that are accurately assessed using a case bootstrapping approach. Examples will use Origin-Destination (OD) modeling in transportation.


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