NONLINEAR MIXED REGRESSION-MODELS

被引：20

作者：

BURNETT, RT

ROSS, WH

KREWSKI, D

机构：

来源：

ENVIRONMETRICS | 1995年 / 6卷 / 01期

关键词：

NONLINEAR REGRESSION; RANDOM EFFECTS; HETEROSCEDASTIC ERRORS; GENERALIZED; ESTIMATING EQUATIONS; OVERDISPERSION; SKIN PAINTING; PAPILLOMA;

D O I：

10.1002/env.3170060108

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this paper we present an estimating equation approach to statistical inference for non-linear random effects regression models for correlated data. With this approach, the distribution of the observations and the random effects need not be specified; only their expectation and covariance structure are required. The variance of the data given the random effects may depend on the conditional expectation. An approximation to the conditional expectation about the fitted value of the random effects is used to obtain closed form expressions for the unconditional mean and covariance of the data. The proposed methods are illustrated using data from a mouse skin painting experiment.

引用

页码：85 / 99

页数：15

共 27 条

[1]

Laird N.M., Ware J.H., Random‐effects models for longitudinal data, Biometrics, 38, pp. 963-974, (1982)

[2]

Chi E.M., Reinsel G.C., Models for longitudinal data with random effects and AR(1) errors, Journal of the American Statistical Association, 84, pp. 452-459, (1989)

[3]

Sheiner L.B., Beal S.L., Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis‐Menten model: routine clinical pharmacokinetic data, Journal of Pharmacokinetics and Biopharmaceutics, 8, pp. 553-571, (1980)

[4]

Vonesh E.F., Carter R.L., Mixed‐effects nonlinear regression for unbalanced repeated measures, Biometrics, 48, pp. 1-17, (1992)

[5]

Lindstrom M.J., Bates D.M., Nonlinear mixed effects models for repeated measures data, Biometrics, 46, pp. 673-687, (1990)

[6]

Racine-Poon A., A Bayesian approach to nonlinear random effects models, Biometrics, 41, pp. 1015-1023, (1985)

[7]

Stiratelli R., Laird N., Ware J., Random effects models for serial observations with binary responses, Biometrics, 40, pp. 961-972, (1984)

[8]

Zeger S.L., Liang K.-Y., Albert P.S., Models for longitudinal data: a generalized estimation equation approach, Biometrics, 44, pp. 1049-1060, (1988)

[9]

Liang K.-Y., Zeger S.L., Longitudinal data analysis using generalized linear models, Biometrika, 73, pp. 13-22, (1986)

[10]

Breslow N.E., Clayton D.G., Approximate inference in generalized linear mixed models, Journal of the American Statistical Association, 88, pp. 9-25, (1993)

← 1 2 3 →