Statistical methods for a general theory of all-or-none learning

Greeno and Steiner have shown that a three state Markov Chain with a single absorbing state is equivalent to many of the current formalizations of All-or-None learning theories. Distribution statistics and other summary statistics are derived from the general model. Expressions for the maximum likelihood estimators of its parameters and the sampling variances of the estimates are presented. Likelihood ratio tests for several different null hypotheses are derived. These tests permit one to evaluate the usual null hypotheses in terms of the parameters of a process model.This research was supported in part by a USPHS Predoctoral Research Fellowship, 1-F1-MH-31-289-01, by a grant from the Graduate School of the University of Texas, and by grant HD-02212-01 from the National Institute of Child Health and Human Development to Dr. John Theios.     

The estimation of variance-covariance and correlation matrices from incomplete data

Employing simulated data, several methods for estimating correlation and variance-covariance matrices are studied for observations missing at random from data matrices. The effect of sample size, number of variables, percent of missing data and average intercorrelations of variables are examined for several proposed methods.The author is indebted to Professors Leonard A. Marascuilo, Gus W. Haggstrom, especially Henry F. Kaiser for their invaluable suggestions throughout this work. Appreciation is also extended to the Computer Center facility of the University of California at Berkeley for the use of computer time to complete the necessary computations.