Simultaneous Confidence Intervals for the Linear Functions of Expected Mean Squares Used in Generalizability Theory

1986 ◽  
Vol 11 (3) ◽  
pp. 197-205
Author(s):  
John F. Bell

This paper demonstrates a method, derived by Khuri (1981) , of constructing simultaneous confidence intervals for functions of expected values of mean squares obtained when analyzing a balanced design by a random effects linear model. The method may be applied to obtain confidence intervals for the variance components and other linear functions of the expected mean squares used in generalizability theory, with probability of simultaneous coverage guaranteed to be greater than or equal to the specified confidence coefficient. The Khuri intervals are compared with the approximate intervals obtained by using Satterthwaite’s (1941 , 1946) method in conjunction with Bonferroni’s inequality.

2012 ◽  
Vol 51 (1) ◽  
pp. 67-73
Author(s):  
Hiroto Hyakutake

ABSTRACT There are several linear and nonlinear models for analyzing repeated measurements. The mean response for an individual depends on the regression parameters specific to that individual. One of the simple forms is the sum of vectors of fixed parameters and random effects. When the models with mixed effects for several groups are parallel, pairwise comparisons of level differences are considered. For the comparisons, approximate simultaneous confidence intervals are given.


1970 ◽  
Vol 67 (2) ◽  
pp. 365-370 ◽  
Author(s):  
Saibal Banerjee

AbstractIt is shown that given k samples of nj units from it is possible to construct simultaneous confidence intervals for two given linear functions of population means, (where cij are known constants), when population variances are not equal.


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