N.Y. Best quadrati.c unbiased estimators (BQUE ' s) of variance components from unbalanced data in the one-way classification random model are derived under zero mean and normality as~ptions. An estima~r or the between-class variance is also developed for the non-zero mean case fron analogy with the zero mean situation. These estirnators a.re fullctions of tbe ratio of the population variances, p = a!(a ~. Nwnerical studies indicate that for,badly unbalanced data and for values of p larger than one, estimators of • 2 having a variance less than that or the analysis of variance estimator can be obtained by substituting even a rather inaccurately predetermined value of p into the BQUE of a2
Formulas are developed for calculating expected mean squares in variance components models whose eff...
This thesis is concerned with the problem of variance components estimation and its applications in ...
AbstractThe variance of a quadratic function of the random variables in a linear model is minimized ...
An unbalanced mixed linear model with two variance components is considered, one variance component ...
This issue was undated. The date given is an estimate.41 pages, 1 article*Best Quadratic Unbiased Es...
Variance components estimation originated with estimating error variance in analysis of variance by ...
AbstractAn unbalanced mixed linear model with two variance components is considered, one variance co...
Several methods are available in literature for estimating the variance components in mixed effects ...
A nonnegative variance estimation procedure is suggested for an unbalanced data where two factors ar...
There is considerable amount of literature dealing with inference about the parameters in a heterosc...
This paper has two di~?tinct parts. The first is a brief account of early work (1939-1953) on varian...
46 pages, 1 article*Detailed Derivations for Minque and Mivque Estimation of Variance Components fro...
51 pages, 1 article*On the Distribution of Variance Component Estimates in the Unbalanced One-Way Cl...
Typescript (photocopy).The estimation of variance components in random and mixed factorial analysis ...
The purpose of this paper is to introduce some recent developments in variance component estimation ...
Formulas are developed for calculating expected mean squares in variance components models whose eff...
This thesis is concerned with the problem of variance components estimation and its applications in ...
AbstractThe variance of a quadratic function of the random variables in a linear model is minimized ...
An unbalanced mixed linear model with two variance components is considered, one variance component ...
This issue was undated. The date given is an estimate.41 pages, 1 article*Best Quadratic Unbiased Es...
Variance components estimation originated with estimating error variance in analysis of variance by ...
AbstractAn unbalanced mixed linear model with two variance components is considered, one variance co...
Several methods are available in literature for estimating the variance components in mixed effects ...
A nonnegative variance estimation procedure is suggested for an unbalanced data where two factors ar...
There is considerable amount of literature dealing with inference about the parameters in a heterosc...
This paper has two di~?tinct parts. The first is a brief account of early work (1939-1953) on varian...
46 pages, 1 article*Detailed Derivations for Minque and Mivque Estimation of Variance Components fro...
51 pages, 1 article*On the Distribution of Variance Component Estimates in the Unbalanced One-Way Cl...
Typescript (photocopy).The estimation of variance components in random and mixed factorial analysis ...
The purpose of this paper is to introduce some recent developments in variance component estimation ...
Formulas are developed for calculating expected mean squares in variance components models whose eff...
This thesis is concerned with the problem of variance components estimation and its applications in ...
AbstractThe variance of a quadratic function of the random variables in a linear model is minimized ...