Meyer, Karin

Houle, David

2013

Approximate lower bound sampling errors of maximum likelihood estimates of covariance components and their linear functions can be obtained from the inverse of the information matrix. For non-linear functions, sampling variances are commonly determined as the variance of their first order Taylor series expansions. This is used to obtain sampling errors for estimates of heritabilities and correlations, and these quantities can be computed with most software performing such analyses. In other instances, however, more complicated functions are of interest or the linear approximation is difficult or inadequate. A pragmatic alternative then is to evaluate sampling characteristics by repeated sampling of parameters from their asymptotic, multivariate normal distribution, calculating the function(s) of interest for each sample and inspecting the distribution across replicates. This paper demonstrates the use of this approach and examines the quality of approximation obtained.

Proceedings of the Association for the Advancement of Animal Breeding and Genetics, v.20, p. 523-526

9780473260569

1328-3227

https://hdl.handle.net/1959.11/14324

en

Association for the Advancement of Animal Breeding and Genetics (AAABG)

Sampling Based Approximation of Confidence Intervals for Functions of Genetic Covariance Matrices

Conference Publication

Publication