Linear Algebra and Matrix Analysis for Statistics
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BeschreibungLinear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic such as infinite-dimensional spaces and provides illustrative examples. The author examines recent developments in diverse fields such as spatial statistics, machine learning, data mining and social network analysis. Complete in its coverage and accessible to students without prior knowledge of linear algebra, the text also includes results that are useful for traditional statistical applications.
PortraitUniversity of Minnesota, Minneapolis, USA Department of Math and Statistics, University of Maryland Baltimore County, USA
PressestimmenThis beautifully written text is unlike any other in statistical science. It starts at the level of a first undergraduate course in linear algebra, and takes the student all the way up to the graduate level, including Hilbert spaces. It is extremely well crafted and proceeds up through that theory at a very good pace. The statistics chapters are added at just the right places to motivate the reader and illustrate the theory. The book is compactly written and mathematically rigorous, yet the style is lively as well as engaging. This elegant, sophisticated work will serve upper level and graduate statistics education well. All and all a book I wish I could have written. --Jim Zidek, University of British Columbia, Vancouver, Canada
Untertitel: 'Chapman & Hall/CRC Texts in Statistical Science'. 10 black & white illustrations, 3 black & white tables. Sprache: Englisch.
Verlag: Taylor & Francis Ltd
Erscheinungsdatum: Juni 2014
Seitenanzahl: 580 Seiten