JSM 2005 - Toronto

Abstract #303534

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: 321
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #303534
Title: Model-based Sampling Selection under an Anisotropic Population
Author(s): Chang-Tai Chao*+ and Feng-Min Lin
Companies: National Cheng Kung University and National Cheng Kung University
Address: Department of Statistics, Tainan City, 701, Taiwan
Keywords: Anisotropic Population ; Spatial Sampling ; Model-Based Sampling ; Eigensystem
Abstract:

To select n sampling units out of N population units to predict the population quantity of interest in spatial statistics, an appropriate spatial sample design is required to recognize and account for the spatial auto-correlation in the spatial process; for example, a spatial systematic design traditionally would be used for better prediction results. This is, however, only effective under certain population covariance structures, such as an isotropic population model. For more general cases, the optimal sampling strategies can be used to select the optimal sample with which the mean-square error is minimized. Nevertheless, the practical interest of such optimal sampling strategies is seriously restricted by the intensive computational load and model assumption required to select the optimal sample. The object of this study is to construct spatial sampling designs under an anisotropic population to predict the population quantity of interest, such as population mean level, with lower prediction error. The performances of the proposed designs based on the relative efficiency of the proposed designs to simple random sampling will be illustrated with simulation study.

  • 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

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005