Numerical Analysis for Statisticians

  • Kenneth Lange

Part of the Statistics and Computing book series (SCO)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Pages 37-52
  3. Pages 103-114
  4. Pages 115-129
  5. Pages 177-190
  6. Pages 191-206
  7. Pages 207-220
  8. Pages 221-234
  9. Pages 252-268
  10. Pages 286-298
  11. Pages 299-313
  12. Pages 330-343
  13. Back Matter
    Pages 345-356

About this book


This book, like many books, was born in frustration. When in the fall of 1994 I set out to teach a second course in computational statistics to d- toral students at the University of Michigan, none of the existing texts seemed exactly right. On the one hand, the many decent, even inspiring, books on elementary computational statistics stress the nuts and bolts of using packaged programs and emphasize model interpretation more than numerical analysis. On the other hand, the many theoretical texts in - merical analysis almost entirely neglect the issues of most importance to statisticians. TheclosestbooktomyidealwastheclassicaltextofKennedy and Gentle [2]. More than a decade and a half after its publication, this book still has many valuable lessons to teach statisticians. However, upon re?ecting on the rapid evolution of computational statistics, I decided that the time was ripe for an update. The book you see before you represents a biased selection of those topics in theoretical numerical analysis most relevant to statistics. By intent this book is not a compendium of tried and trusted algorithms, is not a c- sumer’s guide to existing statistical software, and is not an exposition of computer graphics or exploratory data analysis. My focus on principles of numerical analysis is intended to equip students to craft their own software and to understand the advantages and disadvantages of di?erent numerical methods. Issues of numerical stability, accurate approximation, compu- tional complexity, and mathematical modeling share the limelight and take precedence over philosophical questions of statistical inference.


Markov chain Monte Carlo Newton's method STATISTICA algorithms expectation–maximization algorithm linear regression optimization

Authors and affiliations

  • Kenneth Lange
    • 1
  1. 1.Departments of Biomathematics and Human GeneticsUCLA School of MedicineLos AngelesUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94979-6
  • Online ISBN 978-0-387-22724-5
  • Series Print ISSN 1431-8784
  • Buy this book on publisher's site