Overview
- Serves as a graduate text for a survey of computational statistics
- Second edition adds material on optimization, MM algorithm, penalty and barrier methods, and model selection via the lasso
- Other major topics are updated
Part of the book series: Statistics and Computing (SCO)
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Table of contents (27 chapters)
Keywords
About this book
Reviews
From the reviews:
"This book provides reasonably good coverage of numerical methods that are important in statistical applications. ...but overall the text serves as a good introduction to computational statistics." - MATHEMATICAL REVIEWS
From the reviews of the second edition:
“The theory and equations are well defined and easy enough to read. … This book gives you all the details you need for choosing formulas and libraries when implementing Fourier Transforms. … this is a good book … .” (Cats and Dogs with Data, maryannedata.wordpress.com, July, 2013)
“The aim and scope of this edition is to provide upper level undergraduate students, graduate students and even researchers the understanding and working knowledge of different numerical methods. … The book is organized sequentially and is well structured. … The book can be served as a textbook and equally as a reference book. … the book will appeal to a broad interdisciplinary research community. It can also successfully be used as a reference book for practitioners, providing concrete examples, data and exercises of statistical applications.” (Technometrics, Vol. 53 (2), May, 2011)
“This is a comprehensive handbook for anyone with an interest in computational statistics, such as instructors, statisticians, modelers, data mining analysts, and software designers. For a reader with good working knowledge of numerical analysis, the book is useful for understanding the advantages and disadvantages of different numerical methods. … also suitable for students interested in refining their knowledge: a list of problems with gradually increasing difficulty is available, in addition to a list of very carefully chosen references (a real support for the reader).” (Dragos Calitoiu, Mathematical Reviews, Issue 2011 g)
“Numerical Analysis for Statisticians is a wonderful book. It provides most of the necessary background in calculusand enough algebra to conduct rigorous numerical analyses of statistical problems. … I simply enjoyed Numerical Analysis for Statisticians from beginning until end. … Numerical Analysis for Statisticians also is recommended for more senior researchers, and not only for building one or two courses on the bases of statistical computing. … an essential book to hand to graduate students as soon as they enter a statistics program.” (Christian Robert, Chance, Vol. 24 (4), 2011)Â
Authors and Affiliations
Bibliographic Information
Book Title: Numerical Analysis for Statisticians
Authors: Kenneth Lange
Series Title: Statistics and Computing
DOI: https://doi.org/10.1007/978-1-4419-5945-4
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC 2010
Hardcover ISBN: 978-1-4419-5944-7Published: 15 June 2010
Softcover ISBN: 978-1-4614-2612-7Published: 05 September 2012
eBook ISBN: 978-1-4419-5945-4Published: 17 May 2010
Series ISSN: 1431-8784
Series E-ISSN: 2197-1706
Edition Number: 2
Number of Pages: XX, 600
Topics: Econometrics, Probability Theory and Stochastic Processes, Statistics and Computing/Statistics Programs