Linear Algebra and Matrix Analysis for Statistics (Chapman & Hall/CRC Texts in Statistical Science)

Críticas:

"This would be a reasonable candidate for use in a standard linear algebra course, even at institutions with no statistics majors. … The proofs are very detailed and the authors bind the argument together with clear text that flows beautifully. … Some linear algebra courses put a greater emphasis on concrete applications or on using software to get computations done. Other texts treat linear algebra as a branch of abstract algebra and allow spaces over arbitrary fields. This book is a strong contender for the vast majority of linear algebra courses that fall between those two extremes."
—MAA Reviews, October 2014

"This 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 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

Críticas:

This 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

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