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Fundamentals of High-Dimensional Statistics

Johannes Lederer
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      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.
      Format: Hardback CONTRIBUTORS: Johannes Lederer EAN: 9783030737917 COUNTRY: Switzerland PAGES: WEIGHT: 812 g HEIGHT: 235 cm
      PUBLISHED BY: Springer Nature Switzerland AG DATE PUBLISHED: 2021-11-17 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Database Administration & Management, COMPUTERS / Information Theory, COMPUTERS / Mathematical & Statistical Software, MATHEMATICS / Probability & Statistics / General WIDTH: 155 cm SPINE:

      Book Themes:

      Probability and statistics, Mathematical and statistical software, Databases, Machine learning

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      Johannes Lederer is a Professor of Statistics at the Ruhr-University Bochum, Germany. He received his PhD in mathematics from the ETH Zürich and subsequently held positions at UC Berkeley, Cornell University, and the University of Washington. He has taught high-dimensional statistics to applied and mathematical audiences alike, e.g. as a Visiting Professor at the Institute of Statistics, Biostatistics, and Actuarial Sciences at UC Louvain, and at the University of Hong Kong Business School.
      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.
      Format: Hardback CONTRIBUTORS: Johannes Lederer EAN: 9783030737917 COUNTRY: Switzerland PAGES: WEIGHT: 812 g HEIGHT: 235 cm
      PUBLISHED BY: Springer Nature Switzerland AG DATE PUBLISHED: 2021-11-17 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Database Administration & Management, COMPUTERS / Information Theory, COMPUTERS / Mathematical & Statistical Software, MATHEMATICS / Probability & Statistics / General WIDTH: 155 cm SPINE:

      Book Themes:

      Probability and statistics, Mathematical and statistical software, Databases, Machine learning

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      Johannes Lederer is a Professor of Statistics at the Ruhr-University Bochum, Germany. He received his PhD in mathematics from the ETH Zürich and subsequently held positions at UC Berkeley, Cornell University, and the University of Washington. He has taught high-dimensional statistics to applied and mathematical audiences alike, e.g. as a Visiting Professor at the Institute of Statistics, Biostatistics, and Actuarial Sciences at UC Louvain, and at the University of Hong Kong Business School.

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