(See also TM 501 087, TM 501 088, TM 501 090.)

(See also TM 501 087, TM 501 088, and TM 501 089.)

This paper discusses Bayesian m-group regression where the groups are arranged in a two-way layout into m rows and n columns, there still being a regression of y on the x's within each group. The mathematical model is then provided as applied to the case where the rows correspond to high schools and the columns to colleges: the predictor variables…

The purpose of this study was to compare five predictor models, including two least-square procedures, two probability weighting (semi-Bayesian) methods, and a Bayesian model developed by Lindley. (See also TM 501 088, TM 501 089, and TM 501 090) (Author/NE)

(See also TM 501 087, TM 501 089 and TM 501 090.)

The feasibility and effectiveness of a Bayesian method for estimating regressions in m groups is studied by application of the method to data from the Basic Research Service of The American College Testing Program. Evidence supports the belief that in many testing applications the collateral information obtained from each subset of m-1 colleges…

Bayesian and empirical Bayes approaches to shrinkage estimation of regression coefficients and uses of these in prediction (i.e., analyzing intelligence test data of children with learning problems) are investigated. The two methods are consistently better at predicting response variables than are either least squares or least absolute deviations.…

Least squares and Bayes methods were used in a cross validation study conducted for comparison purposes. The study applies to situations with the following conditions: predictor data are given on the same scales; criterion data may be given on different scales; and it is necessary to pool data even though criterion scale differences exist. Such a…

Classical statistical methods and the small enrollments in graduate departments have constrained the Graduate Record Examinations (GRE) Validity Study Service to providing only validities for single predictors. Estimates of the validity of two or more predictors, used jointly, are considered too unreliable because the corresponding prediction…

A Bayesian prediction strategy is outlined in which antecedent measures are divided into two subgroups. One subgroup is used to discriminate among criterion groups, the second to provide normal linear predictions for each group. Individualized regression constants are subsequently obtained by computing probabilities of group membership from the…