TY  - GEN
AU  - Montesinos López,Osval Antonio
AU  - Montesinos López,Abelardo
AU  - Crossa,José
TI  - Multivariate Statistical Machine Learning Methods for Genomic Prediction
SN  - 978-3-030-89010-0
PY  - 2022///
CY  - Cham
PB  - Springer Nature
KW  - Agricultural science
KW  - bicssc
KW  - Life sciences: general issues
KW  - Botany & plant sciences
KW  - Animal reproduction
KW  - Probability & statistics
KW  - open access
KW  - Statistical learning
KW  - Bayesian regression
KW  - Deep learning
KW  - Non linear regression
KW  - Plant breeding
KW  - Crop management
KW  - multi-trait multi-environments models
N1  - Open Access
N2  - 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
UR  - https://library.oapen.org/bitstream/20.500.12657/52837/1/978-3-030-89010-0.pdf
UR  - https://directory.doabooks.org/handle/20.500.12854/78249
ER  -