BIRS Workshop Lecture Videos
Optimal designs for individual prediction in multiple group random coefficient regression models Prus, Maryna
Random coefficient regression (RCR) models are popular in many fields of statistical application; especially in biosciences and medical research. In these models observational units (individuals) are assumed to come from the same population with an unknown population mean and differ from each other by individual random parameters. Besides the estimation of the population mean parameter, the prediction of the individual response is often of prior interest. In the particular case of multiple group RCR models individuals in different groups get different kinds of treatment. If group sizes are fixed and the unknown mean parameters may differ from group to group, statistical analysis can be performed in each group separately (see Prus, M.: Optimal Designs for the Prediction in Hierarchical Random Coefficient Regression Models. Ph.D. thesis, Otto-von-Guericke University Magdeburg (2015).). This talk presents analytical results for optimal group sizes for the prediction of the individual parameters in multi group RCR models with a common population mean for all individuals across all groups.
Item Citations and Data
Attribution-NonCommercial-NoDerivatives 4.0 International