Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation. - Wikidata - awgldk - TriplyDB

Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation.

Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation.

http://www.wikidata.org/entity/Q37655720

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The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing.Genome-wide prediction methods for detecting genetic effects of donor chromosome segments in introgression populations.A Ranking Approach to Genomic Selection.Genomic Prediction of Testcross Performance in Canola (Brassica napus).Genomic selection across multiple breeding cycles in applied bread wheat breeding Genomic selection of crossing partners on basis of the expected mean and variance of their derived lines.Genomic prediction applied to high-biomass sorghum for bioenergy production.

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Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation.

http://www.wikidata.org/entity/Q37655720

description

article científic

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article scientifique

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articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on 20 March 2014

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vedecký článok

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vetenskaplig artikel

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videnskabelig artikel

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vědecký článek

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Heteroscedastic ridge regressi ...... nd accurate effect estimation.

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Matthias Frisch

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Nina Hofheinz

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P2860

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Prediction of total genetic value using genome-wide dense marker maps

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

Genome-wide distribution of genetic diversity and linkage disequilibrium in elite sugar beet germplasm.

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R.

A novel generalized ridge regression method for quantitative genetics.

Back to basics for Bayesian model building in genomic selection.

Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

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539-546

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scholarly article

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10.1534/G3.113.010025

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2014-03-20T00:00:00Z

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259847864

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24449687

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