The Estimation of Variance Components in Analysis of Variance

1946 ◽  
Vol 2 (1) ◽  
pp. 7 ◽  
Author(s):  
S. Lee Crump
Keyword(s):  
Analysis Of Variance ◽  
Variance Components ◽  
Estimation Of Variance

Correct and incorrect estimation of within-day and between-day variation.

1986 ◽  
Vol 32 (9) ◽  
pp. 1734-1737 ◽  
Author(s):  
M J Bookbinder ◽  
K J Panosian
Keyword(s):  
Statistical Theory ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Total Population ◽  
Total Variance ◽  
Population Variance ◽  
Biased Estimators ◽  
The Impact ◽  
Estimation Of Variance

Abstract Between-day variance is an ambiguous term representing either total variance or pure between-day variance. In either case, it is often incorrectly calculated even though analysis of variance (ANOVA) and other excellent methods of estimation are available. We used statistical theory to predict the magnitude of error expected from using several intuitive approaches to estimation of variance components. We also evaluated the impact of estimating the total population variance instead of pure between-day variance and the impact of using biased estimators. We found that estimates of variance components could be systematically biased by several hundred percent. On the basis of these results, we make recommendations to remove these biases and to standardize precision estimates.

Handling Missing Observations in the Two-way Analysis of Variance with Interaction

1978 ◽  
Vol 14 (4) ◽  
pp. 381-388
Author(s):  
H. A. Abou-El-Fittouh ◽  
E. O. Taha
Keyword(s):  
Analysis Of Variance ◽  
Variance Components ◽  
Missing Values ◽  
Missing Observations ◽  
Missing Value ◽  
Tests Of Significance ◽  
Estimation Of Variance

SUMMARYThree procedures for handling missing values in randomized block experiments with more than one observation per plot are examined, and their effects on the resulting expectations of mean squares in the analysis of variance studied. Using the average of the available observations in the plot in which the missing value occurs has the least effect on the validity of the tests of significance and on the estimation of variance components.

Estimating Variance Components from Mean Squares for Random and Mixed Effects Analysis of Variance Models

1966 ◽  
Vol 22 (2) ◽  
pp. 559-570 ◽  
Author(s):  
Norman S. Endler
Keyword(s):  
Total Variation ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Null Hypothesis ◽  
Theoretical Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Relative Contribution ◽  
Using Data ◽  
Estimation Of Variance

The analysis of variance can be used for: (a) F tests of the null hypothesis; (b) investigating theoretical models; and (c) estimating, from mean squares, the relative contributions of variance components. The methods of estimation of variance components enable the researcher not only to test significance but to attribute the relative contribution (percentage of variance) of each source to the total variation (sum of variance components). Discussion concerns the advantages, disadvantages and limitations of random and mixed effects models. The study concludes that each researcher must logically choose the model which best describes his experiment. Three-way random and mixed effects models with one observation per cell are compared and illustrated using data from a multidimensional personality inventory.

Generalizability Theory: The Software Problem

1985 ◽  
Vol 10 (1) ◽  
pp. 19-29 ◽  
Author(s):  
John F. Bell
Keyword(s):  
Analysis Of Variance ◽  
Variance Components ◽  
Generalizability Theory ◽  
Estimation Of Variance

This paper outlines the problems associated with the estimation of variance components in generalizability analyses using analysis of variance software, and discusses the most useful software currently available for this specialist application: the MIVQUE method of the SAS procedure VARCOMP.

Estimation of variance components in normal linear mixed models with additivity

2015 ◽  
Author(s):  
Dário Ferreira ◽  
Sandra S. Ferreira ◽  
Célia Nunes ◽  
João T. Mexia
Keyword(s):  
Mixed Models ◽  
Variance Components ◽  
Linear Mixed Models ◽  
Estimation Of Variance
Sign in / Sign up

Export Citation Format

Share Document