A Comparison of Estimators in Mixed Models

• 발행기관: 한국자료분석학회
• 저자명: Seongyeon Kim
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.6 No.1, 71~88쪽, 전체 18쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2004.02.28

영문 초록

Mixed models are frequently used in inference for the repeated measurement data of biological and biomedical studies. In this paper, we consider the efficiency of the estimated generalized least squares estimator for the linear mixed model. We find that it works well with respect to consistency and empirical efficiency and has similar precision with the generalized least squares estimator. For the nonlinear mixed model, we notice that the approximate extended least squares estimator based on linearization may have serious bias in mean parameter estimates, especially when the variability in random coefficients is large. On the other hand, the extended least squares estimator using Monte Carlo method produces estimates close to that of exact extended least squares estimator and both procedures yield good estimates and confidence intervals for the mean parameters.

목차

1. Introduction

2. Linear Case

3. Nonlinear Case

4. Conclusion

References

키워드

mixed model estimated generalized least squares estimator extended least squares estimator using Monte Carlo method