Showing papers on "Mixed model published in 1970"

Estimation of variance and covariance components in the mixed model

Abstract: A direct method of computing the coefficient of a variance or covariance component in the expectation of a mean square or mean product when the inverse of the coefficient matrix is available is presented for use with models containing only non-interacting sets of random effects in addition to error. These models may contain any number of sets of fixed effects including interactions and partial regressions for continuous variables. Shortcut computational procedures are presented for the estimation of components of variance and covariance when one set of random effects interacts with one or two sets of fixed main effects and subclass frequencies are unequal. A computational example is given for the two-way classification without interaction and one is available in mimeograph form from the author for the three-way classification with interactions.

On Some Tests in Multivariate Mixed Effects Model with Incomplete Data

Abstract: WHILE testing the hypotheses regarding various components of the multivariate fixed, random or mixed model, we generally assume that each of the observations is made on p characters ( p > 1). Under this situation standard tests are available (Anderson (1958), Roy and Gnanadesikan (1959), Gnanadesikan (1956), Roy and Roy (1958), Chakravorti (1968)). However, if it so happens that all these p characters can be measured at some levels, while at other levels some characters are omitted or the experimental conditions are such that they cannot be measured at these levels, then in this case the data remain incomplete. In this article we shall discuss some of the problems of testing of hypotheses in the later case stated above. To discuss the problems we have considered two-way classified multivariate mixed effects model with one observation per cell.