Knowledge Center Catalog

A robust Bayesian genome-based median regression model (Record no. 60854)

MARC details
000 -LEADER
fixed length control field 02580nab a22003497a 4500
001 - CONTROL NUMBER
control field 60854
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240919020951.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190823s2019 gw |||p|op||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0040-5752
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1432-2242 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1007/s00122-019-03303-6
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 1# - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 2702
Personal name Montesinos-Lopez, A.
245 12 - TITLE STATEMENT
Title A robust Bayesian genome-based median regression model
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Berlin (Germany) :
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. 2019.
500 ## - GENERAL NOTE
General note Peer review
520 ## - SUMMARY, ETC.
Summary, etc. Key message: Current genome-enabled prediction models assumed errors normally distributed, which are sensitive to outliers. We propose a model with errors assumed to follow a Laplace distribution to deal better with outliers. Abstract: Current genome-enabled prediction models use regressions that fit the expected value (mean) of a response variable with errors assumed normally distributed, which are often sensitive to outliers, either genetic or environmental. For this reason, we propose a robust Bayesian genome median regression (BGMR) model that fits regressions to the medians of a distribution, with errors assumed to follow a Laplace distribution to deal better with outliers. The BGMR model was evaluated under a Bayesian framework with Markov Chain Monte Carlo sampling using a location–scale mixture representation of the Laplace distribution. The BGMR was implemented with two simulated and two real genomic data sets, and we compared its prediction performance with that of a conventional genomic best linear unbiased prediction (GBLUP) model and the Laplace maximum a posteriori (LMAP) method. The prediction accuracies of BGMR were higher than those of the GBLUP and LMAP methods when there were outliers. The BGMR model could be useful to breeders who need to predict and select genotypes based on data with unknown outliers.
546 ## - LANGUAGE NOTE
Language note Text in English
591 ## - CATALOGING NOTES
Affiliation Montesinos-Lopez, O.A. : Not in IRS Staff list, no CIMMYT Affiliation
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Genomes
Miscellaneous information AGROVOC
Source of heading or term
9 (RLIN) 1131
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 5834
Topical term or geographic name as entry element Regression analysis
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 4013
Topical term or geographic name as entry element Bayesian theory
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 4859
Topical term or geographic name as entry element Models
700 1# - ADDED ENTRY--PERSONAL NAME
Field link and sequence number I1706800
9 (RLIN) 2700
Personal name Montesinos-Lopez, O.A.
Miscellaneous information Genetic Resources Program
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 10315
Personal name Villa-Diharce, E.R.
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 7797
Personal name Gianola, D.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Crossa, J.
Miscellaneous information Genetic Resources Program
Field link and sequence number CCJL01
9 (RLIN) 59
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication Berlin (Germany) : Springer, 2019.
Related parts v. 132, no. 5, p. 1587-1606
Title Theoretical and Applied Genetics
Record control number u444762
International Standard Serial Number 0040-5752
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Article
Suppress in OPAC No
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
08/23/2019   08/23/2019 Article Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 08/23/2019

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