What is restricted maximum likelihood estimation?
In statistics, the restricted (or residual, or reduced) maximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that …
How is restricted maximum likelihood used in statistics?
Restricted maximum likelihood. In statistics, the restricted (or residual, or reduced) maximum likelihood ( REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data,…
What is the REML likelihood of a mixed model?
More technically, the REML likelihood is a likelihood of linear combinations of the original data: instead of the likelihood of y, we consider the likelihood of K y, where the matrix K is such that E [ K y] = 0. REML estimation is often used in the more complicated context of mixed models.
What are restricted maximum likelihood estimators in Hartley AUD Rao?
“The maximum likelihood (ML) procedure of Hartley aud Rao is modified by adapting a transformation from Patterson and Thompson which partitions the likelihood render normality into two parts, one being free of the fixed effects. Maximizing this part yields what are called restricted maximum likelihood (REML) estimators.”
How is the likelihood function calculated in REML?
In the case of variance component estimation, the original data set is replaced by a set of contrasts calculated from the data, and the likelihood function is calculated from the probability distribution of these contrasts, according to the model for the complete data set. In particular, REML is used as a method for fitting linear mixed models.