Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning

Karin Meyer

Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning

Karin Meyer

Animal Genetics and Breeding Unit

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A scheme for penalized estimation of genetic covariance matrices free from tuning - using default settings for the strength or penalization - is described and its efficacy is demonstrated by simulation. Estimates of genetic covariance matrices, ΣG, are known to be afflicted by substantial sampling errors, increasing markedly with the number of traits considered. 'Regularization', i.e. modification of estimators to reduce sampling variation at the expense of a small, additional bias, has been advocated to obtain estimates closer to the population values. An early suggestion by Hayes and Hill (1981, 'bending') has been to shrink the canonical eigenvalues... towards their mean.

Original language	English
Title of host publication	Proceedings of the Association for the Advancement of Animal Breeding and Genetics
Editors	Kim Bunter, Tim Byrne, Hans Daetwyler, Susanne Hermesch, Kathryn Kemper, James Kijas, David Nation, Wayne Pitchford, Suzanne Rowe, Matt Shaffer, Alison van Eenennaam
Place of Publication	Armidale, Australia
Publisher	Association for the Advancement of Animal Breeding and Genetics (AAABG)
Pages	278-281
Volume	21
ISBN (Print)	9780646945545
Publication status	Published - 2015
Event	AAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics - Lorne, Australia Duration: 28 Sept 2015 → 30 Sept 2015

Conference

Conference	AAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics
City	Lorne, Australia
Period	28/09/15 → 30/09/15

Keywords

Animal Breeding

Access to Document

Handle.net

Persistent link

Cite this

Meyer, K. (2015). Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning. In K. Bunter, T. Byrne, H. Daetwyler, S. Hermesch, K. Kemper, J. Kijas, D. Nation, W. Pitchford, S. Rowe, M. Shaffer, & A. van Eenennaam (Eds.), Proceedings of the Association for the Advancement of Animal Breeding and Genetics (Vol. 21, pp. 278-281). Association for the Advancement of Animal Breeding and Genetics (AAABG). http://www.aaabg.org/aaabghome/AAABG21papers/Meyer21278.pdf

Meyer, Karin. / Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning. Proceedings of the Association for the Advancement of Animal Breeding and Genetics. editor / Kim Bunter ; Tim Byrne ; Hans Daetwyler ; Susanne Hermesch ; Kathryn Kemper ; James Kijas ; David Nation ; Wayne Pitchford ; Suzanne Rowe ; Matt Shaffer ; Alison van Eenennaam. Vol. 21 Armidale, Australia : Association for the Advancement of Animal Breeding and Genetics (AAABG), 2015. pp. 278-281

@inproceedings{4cd9cd3a39764b4ebf478ea0c2bcc492,

title = "Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning",

abstract = "A scheme for penalized estimation of genetic covariance matrices free from tuning - using default settings for the strength or penalization - is described and its efficacy is demonstrated by simulation. Estimates of genetic covariance matrices, ΣG, are known to be afflicted by substantial sampling errors, increasing markedly with the number of traits considered. 'Regularization', i.e. modification of estimators to reduce sampling variation at the expense of a small, additional bias, has been advocated to obtain estimates closer to the population values. An early suggestion by Hayes and Hill (1981, 'bending') has been to shrink the canonical eigenvalues... towards their mean.",

keywords = "Animal Breeding",

author = "Karin Meyer",

year = "2015",

language = "English",

isbn = "9780646945545",

volume = "21",

pages = "278--281",

editor = "Kim Bunter and Tim Byrne and Hans Daetwyler and Susanne Hermesch and Kathryn Kemper and James Kijas and David Nation and Wayne Pitchford and Suzanne Rowe and Matt Shaffer and \{van Eenennaam\}, Alison",

booktitle = "Proceedings of the Association for the Advancement of Animal Breeding and Genetics",

publisher = "Association for the Advancement of Animal Breeding and Genetics (AAABG)",

note = "AAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics ; Conference date: 28-09-2015 Through 30-09-2015",

}

Meyer, K 2015, Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning. in K Bunter, T Byrne, H Daetwyler, S Hermesch, K Kemper, J Kijas, D Nation, W Pitchford, S Rowe, M Shaffer & A van Eenennaam (eds), Proceedings of the Association for the Advancement of Animal Breeding and Genetics. vol. 21, Association for the Advancement of Animal Breeding and Genetics (AAABG), Armidale, Australia, pp. 278-281, AAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics, Lorne, Australia, 28/09/15. <http://www.aaabg.org/aaabghome/AAABG21papers/Meyer21278.pdf>

Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning. / Meyer, Karin.
Proceedings of the Association for the Advancement of Animal Breeding and Genetics. ed. / Kim Bunter; Tim Byrne; Hans Daetwyler; Susanne Hermesch; Kathryn Kemper; James Kijas; David Nation; Wayne Pitchford; Suzanne Rowe; Matt Shaffer; Alison van Eenennaam. Vol. 21 Armidale, Australia: Association for the Advancement of Animal Breeding and Genetics (AAABG), 2015. p. 278-281.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning

AU - Meyer, Karin

PY - 2015

Y1 - 2015

N2 - A scheme for penalized estimation of genetic covariance matrices free from tuning - using default settings for the strength or penalization - is described and its efficacy is demonstrated by simulation. Estimates of genetic covariance matrices, ΣG, are known to be afflicted by substantial sampling errors, increasing markedly with the number of traits considered. 'Regularization', i.e. modification of estimators to reduce sampling variation at the expense of a small, additional bias, has been advocated to obtain estimates closer to the population values. An early suggestion by Hayes and Hill (1981, 'bending') has been to shrink the canonical eigenvalues... towards their mean.

AB - A scheme for penalized estimation of genetic covariance matrices free from tuning - using default settings for the strength or penalization - is described and its efficacy is demonstrated by simulation. Estimates of genetic covariance matrices, ΣG, are known to be afflicted by substantial sampling errors, increasing markedly with the number of traits considered. 'Regularization', i.e. modification of estimators to reduce sampling variation at the expense of a small, additional bias, has been advocated to obtain estimates closer to the population values. An early suggestion by Hayes and Hill (1981, 'bending') has been to shrink the canonical eigenvalues... towards their mean.

KW - Animal Breeding

UR - http://www.aaabg.org/aaabghome/proceedings21.php

M3 - Conference contribution

SN - 9780646945545

VL - 21

SP - 278

EP - 281

BT - Proceedings of the Association for the Advancement of Animal Breeding and Genetics

A2 - Bunter, Kim

A2 - Byrne, Tim

A2 - Daetwyler, Hans

A2 - Hermesch, Susanne

A2 - Kemper, Kathryn

A2 - Kijas, James

A2 - Nation, David

A2 - Pitchford, Wayne

A2 - Rowe, Suzanne

A2 - Shaffer, Matt

A2 - van Eenennaam, Alison

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

CY - Armidale, Australia

T2 - AAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics

Y2 - 28 September 2015 through 30 September 2015

ER -

Meyer K. Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning. In Bunter K, Byrne T, Daetwyler H, Hermesch S, Kemper K, Kijas J, Nation D, Pitchford W, Rowe S, Shaffer M, van Eenennaam A, editors, Proceedings of the Association for the Advancement of Animal Breeding and Genetics. Vol. 21. Armidale, Australia: Association for the Advancement of Animal Breeding and Genetics (AAABG). 2015. p. 278-281

Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning

Abstract

Conference

Keywords

Access to Document

Handle.net

Fingerprint

Cite this