Multilevel Analysis for Repeated Measures Data in Lambs1

Repeated Measures and Multilevel Modeling

Multilevel time series models with applications to repeated measures data.

The analysis of repeated measures data can be conducted efficiently using a two-level random coefficients model. A standard assumption is that the within-individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model fo...

Multivariate Analysis of Repeated Measures Data

ABSTRACT In this paper, we have used SAS software for the multivariate analysis of repeated measures data due to Grizzel and Allen (1969). We have applied four multivariate methods viz MANOVA, Profile Analysis, non-parametric multisample rank sum test and non-parametric multisample median test to analyse two sets of data. The findings of the study reveal that profile analysis gives similar resu...

Discriminant Analysis for Repeated Measures Data: A Review

Discriminant analysis (DA) encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis) and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis). In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Sp...

Robust descriptive discriminant analysis for repeated measures data

Robust repeated measures discriminant analysis (RMDA) procedures based on parsimonious covariance structures were developed using trimmed estimators. The e ects of non-normality, covariance structure, and mean con guration on bias and root mean square error (RMSE) of RMDA coe cients were studied using Monte Carlo techniques. The bias and RMSE values of robust RMDA coe cients were at least 10% a...