Results 121 to 130 of about 18,285 (163)

Regularized REML for Estimation in Heteroscedastic Regression Models

Advances in Intelligent and Soft Computing, 2011
In this paper, we propose a regularized restricted maximum likelihood(REML) method for simultaneous variable selection in heteroscedastic regression models. Under certain regularity conditions, we establish the consistency and asymptotic normality of the resulting estimator.
Zhongzhan Zhang, Dengke Xu, Xu Dengke
exaly   +2 more sources

The application of REML in clinical trials

Statistics in Medicine, 1994
AbstractResidual maximum likelihood (REML) is a technique for estimating variance components in multi‐classified data. In contrast to analysis of variance it can be routinely applied to unbalanced data and avoids some of the problems of biased variance estimates found with standard maximum likelihood estimation.
H K, Brown, R A, Kempton
openaire   +2 more sources

Modifications of REML algorithm for HGLMs

Statistics and Computing, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Woojoo Lee, Youngjo Lee 0001
openaire   +1 more source

Extended-REML estimators

Journal of Applied Statistics, 2003
Restricted likelihood was originally introduced as the criterion for the estimation of dispersion components in normal mixed linear models. Lee & Nelder (2001a) showed that it can be extended to a much wider class of models via double extended quasi-likelihood.
Youngjo Lee, John Nelder
openaire   +1 more source

Reml estimation for repeated measures analysis

Journal of Statistical Computation and Simulation, 1991
Models for repeated measures or growth curves consist of a mean response plus error and the errors are usually correlated. Both maximum likelihood and residual maximum likelihood (REML) estimators of a regression model with dependent errors are derived for cases in which the variance matrix of the error model admits a convenient Cholesky factorisation.
McGilchrist, C. A, Cullis, Brian R
exaly   +3 more sources

REML‐Based Diallel Analysis

Crop Science, 2011
ABSTRACTDiallel analyses can give different results depending on the model and the experimental design. A unified framework to fit and compare different models by mixed‐model packages is lacking. We, therefore, present a general diallel model and the required additional restrictions for the genetic variance–covariance structure to become equivalent to ...
J. Möhring, A. E. Melchinger, H. P. Piepho   +2 more
openaire   +1 more source

REML Estimation for Survival Models with Frailty

Biometrics, 1993
A method of estimation for generalised mixed models is applied to the estimation of regression parameters in proportional hazards models for failure times when there are repeated observations of failure on each subject. The subject effect is incorporated into the model as a random frailty term.
openaire   +2 more sources

REML Estimation with Exact Covariance in the Logistic Mixed Model

Biometrics, 1993
Residual maximum likelihood (REML) estimation is adapted to certain logistic mixed models for which representation of the unconditional mean as a linear function of the fixed effects is possible. Only the first two moments of the unconditional distribution need be evaluated, and except for the form of the covariance, the maximization algorithm carries ...
Drum, Melinda L., McCullagh, Peter
openaire   +1 more source

REML and ML estimation for clustered grouped survival data

Statistics in Medicine, 2003
AbstractClustered grouped survival data arise naturally in clinical medicine and biological research. For example, in a randomized clinical trial, the variable of interest is the time to occurrence of a certain event with or without a new treatment and the data are collected from possibly correlated subjects from independent clusters.
Ip, D, Lam, KF
openaire   +4 more sources

A direct derivation of the REML likelihood function

Statistical Papers, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
exaly   +3 more sources