About: Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction Goto Sponge NotDistinct Permalink

An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

article

Attributes	Values
rdf:type	Item
description	article (en) wetenschappelijk artikel (nl) article scientifique publié en 2015 (fr) наукова стаття, опублікована в жовтні 2015 (uk)
publication date	wds:Q58896000-856B2BA1-2107-4731-ACC8-FB86EDE7369F
publication date	2015-10-07 00:00:00Z (xsd:dateTime)
exact match	wds:Q58896000-A0E52FC3-44B4-448C-BE16-78DBEC0F119C
exact match	https://scigraph.springernature.com/pub.10.1007/s13253-015-0229-y
cites work	wds:Q58896000-00D50878-9F65-4C61-9CDF-E84B2E2277E6 wds:Q58896000-05FEE1E4-F6B4-432F-98AF-BD1FC1C6C028 wds:Q58896000-09E5B916-8D4F-45FE-BDF8-1F2A347DBA14 wds:Q58896000-282BEF2B-9E86-491C-A9D4-F0C737651EB0 wds:Q58896000-4AE54CCD-F502-4665-A16C-9FF0527E4455 wds:Q58896000-75F566F6-BCA4-471A-8C32-957D2B129ED9 wds:Q58896000-7603B694-49B9-49B2-A729-30D6AA5EDB5E wds:Q58896000-83E956B7-A30D-4CC8-B2FA-8227F059260B wds:Q58896000-903CAD07-2462-47A4-85C9-27EC71957202 wds:Q58896000-92100C43-734D-4A55-A416-790FB8750FCA wds:Q58896000-A0C63A5D-FBBA-4BF0-A73F-B2754B0B6600 wds:Q58896000-A93C2B9B-FDC0-4018-90FE-8B4542DEA14D wds:Q58896000-ABC02356-8F1F-451C-B56C-9A7AACE09A97 wds:Q58896000-AFD0A389-7897-4948-9A91-5B94B8C6DFAF wds:Q58896000-C127E74E-26D5-4883-8A3C-9CAC3EB27B12 wds:Q58896000-C9F0E17D-B3E9-4EA0-B126-53E019203CFA wds:Q58896000-DAEBD486-D41D-492C-B7EB-D052BFD0D128 wds:Q58896000-E42347D0-779B-4623-B5A1-8448C69766E6 wds:Q58896000-E6C5A663-5EED-4EE1-940B-B76DF0826C25 wds:Q58896000-E76BAE90-E2E1-45F0-A208-198190DBCAAA
cites work	Efficient control of population structure in model organism association mapping Priors in whole-genome regression: the bayesian alphabet returns Polygenic modeling with bayesian sparse linear mixed models Genome-enabled prediction of genetic values using radial basis function neural networks Inference from Iterative Simulation Using Multiple Sequences Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits Kernel-based whole-genome prediction of complex traits: a review Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield. Sampling-Based Approaches to Calculating Marginal Densities Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data Diversity Arrays Technology (DArT) for whole-genome profiling of barley Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat Genomic-assisted prediction of genetic value with semiparametric procedures Whole-genome regression and prediction methods applied to plant and animal breeding Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Some results on Tchebycheffian spline functions Efficient methods to compute genomic predictions Genome-wide regression and prediction with the BGLR statistical package Bayesian genomic-enabled prediction as an inverse problem
author name string	wds:Q58896000-749592F3-A572-4DF7-91C1-FE8A2AD948DA wds:Q58896000-A358130F-0264-47EA-9EFD-51920F17FB44 wds:Q58896000-E0A6E1CD-8690-47BC-BB7F-0539F04BBABA wds:Q58896000-F62EEC0C-13EA-40DF-A0D0-086F3F8CC2A3
author name string	Paulino Pérez-Rodríguez José Crossa Jaime Cuevas Sergio Pérez-Elizalde
rdfs:label	Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (en) Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (nl)
skos:prefLabel	Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (en) Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (nl)
name	Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (en) Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (nl)
title	wds:Q58896000-E27A8689-DF96-4ABD-BAA3-CE39A3F0A550
title	Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction (en)
page(s)	wds:Q58896000-E0AA1307-2646-4413-AE91-79014BC89A24
page(s)	512-532
instance of	wds:Q58896000-17D42772-A701-4817-B58A-AB2A07166C4B
instance of	scholarly article
main subject	wds:Q58896000-68204388-ED40-4D83-95FF-8DEBCC2EE80F wds:Q58896000-6DAE3C70-D798-450C-B711-62789FB04511
main subject	Kernel regression applied mathematics
published in	wds:Q58896000-F39E050E-4EF6-420B-9F28-F2EB70124279
published in	Journal of Agricultural, Biological, and Environmental Statistics
issue	wds:Q58896000-9B916DB7-53FE-46C2-8295-F5A362D88CF9
volume	wds:Q58896000-C7EACEE2-2398-4ADE-A903-47BBB90341B7
issue	4
volume	20
DOI	wds:Q58896000-587234A3-6DB7-4AF3-BF3B-D25373FD45FC
DOI	http://dx.doi.org/10.1007/S13253-015-0229-Y
DOI	10.1007/S13253-015-0229-Y
copyright license	wds:Q58896000-ecf3107d-3eab-40fb-ae4a-74d4a2adf15a
maintained by WikiProject	wds:Q58896000-2F789138-BDE7-4C6D-919C-7F8F472517A0
copyright status	wds:Q58896000-8815a6e4-f674-4b2c-9de2-5cc625602970
copyright license	Creative Commons Attribution 4.0 International
maintained by WikiProject	WikiProject Mathematics
copyright status	copyrighted
zbMATH Open document ID	wds:Q58896000-D454194B-7B42-4DA5-9756-AEFADD954E8A
zbMATH Open document ID	1329.62449
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q58896000
is cites work of	Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding Practical application of genomic selection in a doubled-haploid winter wheat breeding program Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population
is cites work of	wds:Q59281380-5722E5D9-C701-4545-8F2E-154DE22EDC8C wds:Q98778679-42500587-8E91-40F8-A9EC-BD6F68353622 wds:Q41598835-143726F3-981F-4C4F-86F1-A6AB39B2A280 wds:Q47242189-383B11C6-DBA6-4A0C-90BC-156285252595

Faceted Search & Find service v1.16.117 as of May 05 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3239 as of May 5 2024, on Linux (x86_64-centos_6-linux-gnu), Single-Server Edition (378 GB total memory, 188 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software