Estimating and testing variance components in a multi-level GLM

Estimating and testing variance components in a multi-level GLM

Most analysis of multi-subject fMRI data is concerned with determining whether there exists a significant population-wide 'activation' in a comparison between two or more conditions. This is typically assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. However, important information can also be obtained by test...

Testing linear forms of variance components bygeneralized xed { level

This report extends the technique of testing single variance components with generalized xed{level tests | in situations when nuisance parameters make exact testing impossible | to the more general way of testing hypotheses on linear forms of variance components. An extension of the deenition of a generalized test variable leads to a generalized xed{level test for arbitrary linear hypotheses on...

estimating variance components of ellipsoidal, orthometric and geoidal

the best quadratic unbiased estimation (bque) of variance components in the gauss-helmert model is used to combine adjustment of gps/levelling and geoid to determine the individual variance components for each of the three height types. through the research, different reasons for achievement of the negative variance components were discussed and a new modified version of the best quadratic unbi...

Estimating Variance Components Using Alternative Minqe's in Selected Unbalanc Designs

Variance components testing in the longitudinal mixed effects model.

This article discusses the asymptotic behavior of likelihood ratio tests for nonzero variance components in the longitudinal mixed effects linear model described by Laird and Ware (1982, Biometrics 38, 963-974). Our discussion of the large-sample behavior of likelihood ratio tests for nonzero variance components is based on the results for nonstandard testing situations by Self and Liang (1987,...