TY - BOOK ID - 28966199 TI - Numerical analysis for statisticians PY - 1999 SN - 0387949798 9780387949796 9780387227245 9786610006885 1280006889 0387227245 PB - New York (N.Y.): Springer, DB - UniCat KW - AA / International- internationaal KW - 304.0 KW - 303.0 KW - Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen. KW - Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). KW - Numerical analysis. KW - Statistique mathématique KW - 519.4 KW - Applied Mathematics KW - Mathematics. KW - Applied mathematics. KW - Engineering mathematics. KW - Applications of Mathematics. KW - Mathematical statistics KW - Numerical analysis KW - 519.2 KW - Mathematics KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Mathematical analysis KW - 519.2 Probability. Mathematical statistics KW - Probability. Mathematical statistics KW - Statistical methods KW - Mathematical statistics. KW - Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) KW - Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen KW - Engineering KW - Engineering analysis UR - https://www.unicat.be/uniCat?func=search&query=sysid:28966199 AB - 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. ER -