Variance Components in the Unbalanced 2-Way Nested Classification

- - , ' Estimation of Ratios of Variance Components in the Unbalanced One Way Random Effects Model

The point estimation of the ratio of variance components and the intraclass correlation coefficient for the unbalanced one way random effects model are considered. ANOVA, REML, ML, MIVQUE, MINQE type, and MU (median unbiased) estimators are compared with respect to their absolute biases and mean squared errors through a simulation study. Explicit, computable expressions with no matrix inversion...

Variance Components Estimation in a K-way Nested Random Effect Models

This work aims to presented the k way nested random models (designs) and discuss the estimators for the variance components in this kind of models presented by Henderson (1953), proposing condition concern their existence, as well as its two proposed modifications presented by Khattree (1999). At the first proposal, where he choose to sacrifice the unbiasedness of the Henderson's one to preserv...

The unbalanced nested error component regression model

This paper considers a nested error component model with unbalanced data and proposes simple analysis of variance (ANOVA), maximum likelihood (MLE) and minimum norm quadratic unbiased estimators (MINQUE)-type estimators of the variance components. These are natural extensions from the biometrics, statistics and econometrics literature. The performance of these estimators is investigated by mean...

Efficient moment calculations for variance components in large unbalanced crossed random effects models

Large crossed data sets, described by generalized linear mixed models, have become increasingly common and provide challenges for statistical analysis. At very large sizes it becomes desirable to have the computational costs of estimation, inference and prediction (both space and time) grow at most linearly with sample size. Both traditional maximum likelihood estimation and numerous Markov cha...

Analysis of Variance for a One-Way Classification of Data