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Numerical Analysis for Statisticians (Statistics and Computing) epub download

by Kenneth Lange


Numerical Analysis for Statisticians, by Kenneth Lange, is a wonderful book. 34 people found this helpful.

Numerical Analysis for Statisticians, by Kenneth Lange, is a wonderful book. It provides most of the necessary background in calculus and some algebra to conduct rigorous numerical analyses of statistical problems. This includes expansions, eigen-analysis, optimisation, integration, approximation theory, and simulation, in less than 600 pages.

Statistics and Computing. Numerical Analysis for Statisticians. Numerical Analysis for Statisticians is a wonderful book. It provides most of the necessary background in calculus and enough algebra to conduct rigorous numerical analyses of statistical problems. Authors: Lange, Kenneth. I simply enjoyed Numerical Analysis for Statisticians from beginning until en.

Numerical Analysis for Statisticians can serve as a graduate text for a course surveying computational statistics. With a careful selection of topics and appropriate supplementation, it can be used at the undergraduate level. It contains enough material for a graduate course on optimization theory. Because many chapters are nearly self-contained, professional statisticians will also find the book useful as a reference.

Numerical Analysis for Statisticians can serve as a graduate text for either a one or a two-semester course surveying .

Numerical Analysis for Statisticians can serve as a graduate text for either a one or a two-semester course surveying computational statistics. With a careful selection of topics and appropriate supplementation, it can even be used at the undergraduate level. It contains enough material on optimization theory alone for a one-semester graduate course. Скачать (pdf, . 0 Mb) Читать. Epub FB2 mobi txt RTF. By (author) Kenneth Lange. an essential book to hand to graduate students as soon as they enter a statistics program. Christian Robert, Chance, Vol. 24 (4), 2011) show more.

Электронная книга "Numerical Analysis for Statisticians", Kenneth Lange. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Numerical Analysis for Statisticians" для чтения в офлайн-режиме. With a careful selection of topics and appropriate supplementation, it can be used at the undergraduate level

Numerical Analysis for Statisticians can serve as a graduate text for a course surveying computational statistics. Because many chapters are nearly self-contained, professional statisticians will also find the book useful as a reference

Numerical Methods of Statistics. journals in statistics and computing.

Numerical Methods of Statistics. This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. He is the author of Computational Statistics, Random Number Generation and Monte Carlo Methods, and Matrix Algebra.

Numerical Analysis for Statisticians Lange Kenneth Springer . This book focuses on the principles of numerical analysis.

Numerical Analysis for Statisticians Lange Kenneth Springer 9781441959447 : Presenting aspects of numerical analysis applicable to statisticians, this volume enables students to craft their o. It is suitable for those who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.

Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.

Numerical Analysis for Statisticians (Statistics and Computing) epub download

ISBN13: 978-1441959447

ISBN: 1441959440

Author: Kenneth Lange

Category: Math and Science

Subcategory: Mathematics

Language: English

Publisher: Springer; 2nd ed. 2010 edition (June 15, 2010)

Pages: 600 pages

ePUB size: 1255 kb

FB2 size: 1660 kb

Rating: 4.7

Votes: 214

Other Formats: lit lrf docx mbr

Related to Numerical Analysis for Statisticians (Statistics and Computing) ePub books

Insanity
I bought this book for Kindle, believing that I would get the (recent) second edition - Amazon's Kindle page for this item has as illustration the cover of the second edition! However the ten year old "first edition" was delivered to my Kindle. I later also found out that one could by an eBook second edition directly from Springer publishers for less than half the price.

Fortunately it turned out that it is not so difficult as I thought to "return" a mistaken Kindle purchase, via the "Manage my Kindle" page.

In conclusion: fantastic book, but make sure you get the second edition, and if you want an eBook version, don't buy it on Amazon for the time being!
Insanity
I bought this book for Kindle, believing that I would get the (recent) second edition - Amazon's Kindle page for this item has as illustration the cover of the second edition! However the ten year old "first edition" was delivered to my Kindle. I later also found out that one could by an eBook second edition directly from Springer publishers for less than half the price.

Fortunately it turned out that it is not so difficult as I thought to "return" a mistaken Kindle purchase, via the "Manage my Kindle" page.

In conclusion: fantastic book, but make sure you get the second edition, and if you want an eBook version, don't buy it on Amazon for the time being!
Welen
Somehow, I had missed the first edition of this book and thus I started reading it this afternoon with a newcomer's eyes (obviously, I will not comment on the differences with the first edition, sketched by the author in the Preface). Past the initial surprise of discovering it was a mathematics book rather than an algorithmic book, I became engrossed into my reading and could not let it go! Numerical Analysis for Statisticians, by Kenneth Lange, is a wonderful book. It provides most of the necessary background in calculus and some algebra to conduct rigorous numerical analyses of statistical problems. This includes expansions, eigen-analysis, optimisation, integration, approximation theory, and simulation, in less than 600 pages. It may be due to the fact that I was reading the book in my garden, with the background noise of the wind in tree leaves, but I cannot find any solid fact to grumble about! Not even about the MCMC chapters! I simply enjoyed Numerical Analysis for Statisticians from beginning till end.

From the above, it may sound as if Numerical Analysis for Statisticians does not fulfill its purpose and is too much of a mathematical book. Be assured this is not the case: the contents are firmly grounded in calculus (analysis) but the (numerical) algorithms are only one code away. An illustration (among many) is found in Section 8.4: Finding a Single Eigenvalue, where Kenneth Lange shows how the Raleigh quotient algorithm of the previous section can be exploited to this aim, when supplemented with a good initial guess based on Gerschgorin's circle theorem. This is brilliantly executed in two pages and the code is just one keyboard away. The EM algorithm is immersed into a larger MM perspective. Problems are numerous and mostly of high standards, meaning one (including me) has to sit and think about them. References are kept to a minimum, they are mostly (highly recommended) books, plus a few papers primarily exploited in the problem sections.

While I am reacting so enthusiastically to the book (imagine, there is even a full chapter on continued fractions!), it may be that my French math background is biasing my evaluation and that graduate students over the World would find the book too hard. However, I do not think so: the style of Numerical Analysis for Statisticians is very fluid and the rigorous mathematics are mostly at the level of undergraduate calculus. The more advanced topics like wavelets, Fourier transforms and Hilbert spaces are very well-introduced and do not require prerequisites in complex calculus or functional analysis. (Although I take no joy in this, even measure theory does not appear to be a prerequisite!) On the other hand, there is a prerequisite for a good background in statistics. This book will clearly involve a lot of work from the reader, but the respect shown by Kenneth Lange to those readers will sufficiently motivate them to keep them going till assimilation of those essential notions. Numerical Analysis for Statisticians is also recommended for more senior researchers and not only for building one or two courses on the bases of statistical computing. It contains most of the math bases that we need, even if we do not know we need them! Truly an essential book.
Welen
Somehow, I had missed the first edition of this book and thus I started reading it this afternoon with a newcomer's eyes (obviously, I will not comment on the differences with the first edition, sketched by the author in the Preface). Past the initial surprise of discovering it was a mathematics book rather than an algorithmic book, I became engrossed into my reading and could not let it go! Numerical Analysis for Statisticians, by Kenneth Lange, is a wonderful book. It provides most of the necessary background in calculus and some algebra to conduct rigorous numerical analyses of statistical problems. This includes expansions, eigen-analysis, optimisation, integration, approximation theory, and simulation, in less than 600 pages. It may be due to the fact that I was reading the book in my garden, with the background noise of the wind in tree leaves, but I cannot find any solid fact to grumble about! Not even about the MCMC chapters! I simply enjoyed Numerical Analysis for Statisticians from beginning till end.

From the above, it may sound as if Numerical Analysis for Statisticians does not fulfill its purpose and is too much of a mathematical book. Be assured this is not the case: the contents are firmly grounded in calculus (analysis) but the (numerical) algorithms are only one code away. An illustration (among many) is found in Section 8.4: Finding a Single Eigenvalue, where Kenneth Lange shows how the Raleigh quotient algorithm of the previous section can be exploited to this aim, when supplemented with a good initial guess based on Gerschgorin's circle theorem. This is brilliantly executed in two pages and the code is just one keyboard away. The EM algorithm is immersed into a larger MM perspective. Problems are numerous and mostly of high standards, meaning one (including me) has to sit and think about them. References are kept to a minimum, they are mostly (highly recommended) books, plus a few papers primarily exploited in the problem sections.

While I am reacting so enthusiastically to the book (imagine, there is even a full chapter on continued fractions!), it may be that my French math background is biasing my evaluation and that graduate students over the World would find the book too hard. However, I do not think so: the style of Numerical Analysis for Statisticians is very fluid and the rigorous mathematics are mostly at the level of undergraduate calculus. The more advanced topics like wavelets, Fourier transforms and Hilbert spaces are very well-introduced and do not require prerequisites in complex calculus or functional analysis. (Although I take no joy in this, even measure theory does not appear to be a prerequisite!) On the other hand, there is a prerequisite for a good background in statistics. This book will clearly involve a lot of work from the reader, but the respect shown by Kenneth Lange to those readers will sufficiently motivate them to keep them going till assimilation of those essential notions. Numerical Analysis for Statisticians is also recommended for more senior researchers and not only for building one or two courses on the bases of statistical computing. It contains most of the math bases that we need, even if we do not know we need them! Truly an essential book.
Lestony
Ron Thisted's book on computing algorithms for statisticians was one of the most useful and clearly written texts on the topic. There have also been a few other good ones. Lange brings to the table a more current book that deals with the key new methods such as resampling, Markov chain Monte Carlo, Fourier series and wavelets,the EM algorithm and extensions of it. He also includes useful but uncommon results for power series, exponentiating matrices and continued fraction expansions.
The usual matrix algebra stuff for linear models is also there. You will also find a chapter on nonlinear equations and a chapter on splines. There are asymptotic expansions in Chapter 4 and Edgeworth expansions in Chapter 17. Almost everything that is important in statistical computing today is included.

This book can be used as for a graduate course in statistical computing and is a valuable reference for any statistical researcher.
Lestony
Ron Thisted's book on computing algorithms for statisticians was one of the most useful and clearly written texts on the topic. There have also been a few other good ones. Lange brings to the table a more current book that deals with the key new methods such as resampling, Markov chain Monte Carlo, Fourier series and wavelets,the EM algorithm and extensions of it. He also includes useful but uncommon results for power series, exponentiating matrices and continued fraction expansions.
The usual matrix algebra stuff for linear models is also there. You will also find a chapter on nonlinear equations and a chapter on splines. There are asymptotic expansions in Chapter 4 and Edgeworth expansions in Chapter 17. Almost everything that is important in statistical computing today is included.

This book can be used as for a graduate course in statistical computing and is a valuable reference for any statistical researcher.