03216nam 22006373 450 991052299910332120220207155350.03-030-89010-4(CKB)5100000000193939(MiAaPQ)EBC6855260(Au-PeEL)EBL6855260(OCoLC)1294143848(oapen)https://directory.doabooks.org/handle/20.500.12854/78249(PPN)260834084(EXLCZ)99510000000019393920220207d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMultivariate Statistical Machine Learning Methods for Genomic PredictionChamSpringer Nature2022Cham :Springer International Publishing AG,2022.©2022.1 online resource (707 pages)3-030-89009-0 This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.Agricultural sciencebicsscLife sciences: general issuesbicsscBotany & plant sciencesbicsscAnimal reproductionbicsscProbability & statisticsbicsscopen accessStatistical learningBayesian regressionDeep learningNon linear regressionPlant breedingCrop managementmulti-trait multi-environments modelsAgricultural scienceLife sciences: general issuesBotany & plant sciencesAnimal reproductionProbability & statisticsMontesinos López Osval Antonio1078558Montesinos López Abelardo1078559Crossa José1078560MiAaPQMiAaPQMiAaPQBOOK9910522999103321Multivariate Statistical Machine Learning Methods for Genomic Prediction2590779UNINA01988oam 2200541 450 991071717190332120220218090722.0(CKB)5470000002528495(OCoLC)1291256974(OCoLC)995470000002528495(EXLCZ)99547000000252849520211216d2021 ua 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierCollege closures many impacted borrowers struggled financially despite being eligible for loan discharges: testimony before the Subcommittee on Higher Education and Workforce Investment, Committee on Education and Labor, House of Representatives /statement of Melissa Emrey-Arras[Washington, D.C.] :United States Government Accountability Office,2021.1 online resource (approxiimately 20 pages) color illustrations"GAO-21-105373.""September 2021."Includes bibliographical references.College closures Student loansUnited StatesEvaluationStudent aidUnited StatesEvaluationUniversities and collegesUnited StatesFinanceStudent aidEvaluationfastStudent loansEvaluationfastUniversities and collegesFinancefastUnited StatesfastStudent loansEvaluation.Student aidEvaluation.Universities and collegesFinance.Student aidEvaluation.Student loansEvaluation.Universities and collegesFinance.Emrey-Arras Melissa1398456LVTLVTGPOOCLCOOCLCFGPOBOOK9910717171903321College closures3521346UNINA