Inverse Prediction for Heteroscedastic Response Using Mixed Models Software

• NCJ Number: 252293
• Journal: Communications in Statistics-Simulation and Computation Volume: 46 Issue: 6 Dated: 2017 Pages: 4490-4498
• Author(s): Lynn R. Lamotte; Jeffrey D. Wells
• Date Published: 2017
• Length: 9 pages
• DOI: 10.1080/03610918.2015.1118508
• Grant Number(s): 2013-DN-BX-K042
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Statistical Methods for Combining Multivariate and Categorical Data in Postmortem Interval Estimation

Annotation

Distributions of a response y (height, for example) differ with values of a factor t (such as age). Given a response y* for a subject of unknown t*, the objective of inverse prediction is to infer the value of t* and to provide a defensible confidence set for it. This article describes how versatile models for this problem can be formulated in such a way that the computations can be accomplished with widely available software for mixed models, such as SAS PROC MIXED. Coverage probabilities of confidence sets on t* are illustrated in an example.

Abstract

Training data provide values of y observed on subjects at known values of t. Models relating the mean and variance of y to t can be formulated as mixed (fixed and random) models in terms of sets of functions of t, such as polynomial spline functions. A confidence set on t* can then be had as those hypothetical values of t for which y* is not detected as an outlier when compared to the model fit to the training data. With nonconstant variance, the p-values for these tests are approximate. (Publisher abstract modified)