Robust Estimators for the One-Way Variance Components Model

Abstract. Title of the paper: Variation of lean content in pig beilies of dam lines Magnetic resonance imaging (MRI) was applied to determine the tissue composition of 157 bellies of castrated male pigs belonging to two dam-lines of the German Hybrid Pig Breeding Programme. All pigs were taken from the routine testing scheme. The analysis of carcass traits showed that pigs of line 03 were clearly leaner than those of line 01. Only in backfat thickness no significant difference could be observed. For the belly, a clear difference of 7% was found in percentage of lean. Lean content of the belly was also estimated by means of various multiple regressions using carcass traits. Percentage of lean predicted by the official equation used in German performance testing on the one hand and by means of MRI on the other hand was different particularly in line 03. A correlation of r = 0.67 and r = 0.82 between lean content determined by means of MRI and multiple regression was found for line 01 and line 03, respectively. To study the Variation between sires, variance components were estimated. Differences between sires were higher in lean content of the belly compared to backfat thickness, a trait which has been used in selection for a long time.

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The Intraclass Correlation Coefficient as a Measure of Reliability

Psychological Reports ◽

10.2466/pr0.1966.19.1.3 ◽

1966 ◽

Vol 19 (1) ◽

pp. 3-11 ◽

Cited By ~ 1278

Author(s):

John J. Bartko

Keyword(s):

Correlation Coefficient ◽

Intraclass Correlation Coefficient ◽

Variance Components ◽

Intraclass Correlation ◽

Correlation Coefficients ◽

Frequent Use ◽

Ratio Of Variances ◽

The One ◽

Definition Of ◽

Estimation Of Variance

A procedure for estimating the reliability of sets of ratings in terms of the intraclass correlation coefficient is discussed. The procedure is based upon the analysis of variance and the estimation of variance components. For the one-way classification the intraclass correlation coefficient defined as the ratio of variances can be interpreted as a correlation coefficient. Caution, however, is urged in the application of the definition to a two-way model, i.e., one in which between-rater variance is removed. It is maintained that the frequent use of the standard definition of the one-way intraclass correlation coefficient applied to the two-way classification is not a proper procedure if in fact the coefficient is to be interpreted as a correlation coefficient. Definitions for reliability obtained from the two-way models are given which can legitimately be considered correlation coefficients.

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Quadratic unbiased estimation of variance components for the one-way classification

Biometrika ◽

10.1093/biomet/56.2.313 ◽

1969 ◽

Vol 56 (2) ◽

pp. 313-326 ◽

Cited By ~ 13

Author(s):

DAVID A. HARVILLE

Keyword(s):

Variance Components ◽

Unbiased Estimation ◽

The One ◽

Estimation Of Variance

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Methods of variance component estimation

Czech Journal of Animal Science ◽

10.17221/3933-cjas ◽

2011 ◽

Vol 51 (No. 6) ◽

pp. 227-235 ◽

Cited By ~ 8

Author(s):

D. Rasch ◽

O. Mašata

Keyword(s):

Population Genetics ◽

Variance Components ◽

Variance Component ◽

Practical Interest ◽

Variance Component Estimation ◽

Basic Principles ◽

Advantages And Disadvantages ◽

Component Estimation ◽

The One ◽

Estimation Of Variance

Estimation of variance components is a method often used in population genetics and applied in animal breeding. Even experienced population geneticists nowadays feel lost if confronted with the huge set of different methods of variance component estimation. Especially because there exists no uniformly best method, a decision which method should be used is often difficult to take. This paper gives a complete overview of methods existing in the simplest case of a one-way lay-out and demonstrates some of them by a numerical example for which the true situation is known. Of course, the one-way lay-out is of limited practical interest but can best be used to explain animal scientists the basic principles of the methods. These basic principles are principally also valid for higher classifications. Advantages and disadvantages of the methods are discussed. The symbols used are the standard biometric symbols as given in Rasch et al. (1994). We can say that all the methods offered by SPSS can be recommended.  

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