JSM 2005 Online Program

Abstract #304073

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:	524
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Physical and Engineering Sciences
Abstract - #304073
Title:	Analysis of Window-observation Recurrence Data
Author(s):	Jianying Zuo*+ and Huaiqing Wu and William Q. Meeker
Companies:	Iowa State University and Iowa State University and Iowa State University
Address:	306 Snedecor Hall, Ames, IA, 50011, United States
Keywords:	MCF ; Forecast ; NHPP ; Nonparametric Estimation ; Repairable System Data
Abstract:	Many systems experience recurrent events. Recurrence data are collected to analyze quantities of interest, such as the mean cumulative number of events or the mean cumulative cost of events. Methods of analysis are available for recurrence data with left and/or right censoring. Due to practical constraints, however, recurrence data are sometimes recorded in windows with gaps between the windows. This paper extends existing methods, both nonparametric and parametric, to window-observation recurrence data. The nonparametric estimator requires minimum assumptions, but will be biased if the risk set is not positive over the entire period of interest. There is no such difficulty when using a parametric model for the recurrence data. For cases in which the risk set is zero for some periods of time, we propose a simple method that uses a parametric adjustment to the nonparametric estimator. The methods are illustrated with two numerical examples.

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