Abstract #301646

This is the preliminary program for the 2003 Joint Statistical Meetings in San Francisco, California. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 2-5, 2003); 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.

Back to main JSM 2003 Program page
JSM 2003 Abstract #301646
Activity Number: 27
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #301646
Title: Explicit Likelihood Estimators in Normal Variance-Components Models
Author(s): Shaun S. Wulff*+
Companies: University of Wyoming
Address: PO Box 3332, Laramie, WY, 82071-3332,
Keywords: least squares ; best unbiased estimator ; UMVUE ; ML ; REML ; MINQUE
Abstract:

As computing technology has advanced, maximum likelihood estimation has become more commonly used in the analysis of normal mixed linear models. Maximum likelihood is an appealing approach to estimation because of its wide applicability and its optimal asymptotic properties under fairly general conditions. Some drawbacks to this approach are that it generally requires an iterative algorithm, which can take time to converge, and the properties of the resulting estimators assume large samples. Szatrowski (1980) gave conditions under which the maximum likelihood estimator for the fixed effect vector corresponds to the least squares estimator. El Bassiouni (1983) derived conditions under which the restricted maximum likelihood estimator corresponds to a minimum norm quadratic unbiased estimator. These estimators are easier to calculate, have exact variance expressions, are unbiased, and have uniformly minimum variance. This talk will present extensions of these results by examining less restrictive conditions that apply to only a part of the vector of fixed effects or for only some of the variance components. Examples will be used to demonstrate these conditions.

  • 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 2003 program

JSM 2003 For information, contact meetings@amstat.org or phone (703) 684-1221. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2003