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＜電子ブック＞
Fundamentals of High-Dimensional Statistics : With Exercises and R Labs / by Johannes Lederer
(Springer Texts in Statistics. ISSN:21974136)

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
大きさ	XIV, 355 p. 34 illus., 21 illus. in color : online resource
著者標目	*Lederer, Johannes author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Big data LCSH:Artificial intelligence—Data processing LCSH:Mathematical statistics—Data processing LCSH:Machine learning LCSH:Biometry FREE:Statistical Theory and Methods FREE:Big Data FREE:Data Science FREE:Statistics and Computing FREE:Machine Learning FREE:Biostatistics
一般注記	Preface -- Notation -- Introduction -- Linear Regression -- Graphical Models -- Tuning-Parameter Calibration -- Inference -- Theory I: Prediction -- Theory II: Estimation and Support Recovery -- A Solutions -- B Mathematical Background -- Bibliography -- Index. This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience HTTP:URL=https://doi.org/10.1007/978-3-030-73792-4

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