Fundamentals of high-dimensional statistics: with exercises and R labs

By:

Lederer, Johannes C

Material type: Text

TextPublication details: Springer Switzerland 2022Description: xi, 355 pISBN:

9783030737917

Subject(s):

Mathematical statistics

DDC classification:

519.5 LED

Summary: About this book 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.

List(s) this item appears in: Operation & quantitative Techniques | Finance & Accounting

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.5 LED (Browse shelf(Opens below))	1	Available		002807

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Previous	519.5 JAM An introduction to statistical learning: with applications in R	519.5 KOL An introduction to nonparametric statistics	519.5 KRI Statistics and data analysis for social science	519.5 LED Fundamentals of high-dimensional statistics: with exercises and R labs	519.5 LEV Statistics for management	519.5 LEV Statistics for management	519.5 LEV Business statistics: a first course	Next

About this book
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.

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