Prediction in Multilevel Models

Prediction in Multilevel Models

Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in the jth group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed b...

Prediction Intervals for Multilevel Models

There are many methods for constructing prediction intervals for differences in observations. This document will explain several of these methods in the context of different models using data from a kiwi yield experiment as an example. 1. KIWI DATA In an experiment (discussed in more detail in class), kiwi yields were measured in a designed experiment. There were three blocks (north, east, and ...

Prediction in multilevel generalized linear models

We discuss prediction of random effects and of expected responses in multilevel generalized linear models. Prediction of random effects is useful for instance in small area estimation and disease mapping, effectiveness studies and model diagnostics. Prediction of expected responses is useful for planning, model interpretation and diagnostics. For prediction of random effects, we concentrate on ...

LinearModels Decomposition of Prediction Error in Multilevel Models

We present a decomposition of prediction error for the multilevel model in the context of predicting a future observable y∗j in the jth group of a hierarchical dataset. The multilevel prediction rule is used for prediction and the components of prediction error are estimated via a simulation study that spans the various combinations of level-1 (individual) and level-2 (group) sample sizes and d...

I-30: Prediction Models in IVF