MARC Record from harvard_bibliographic_metadata

LEADER: 03461nam a22004095a 4500
001 014157781-9
005 20141003190226.0
008 130725s1992 xxu| o ||0| 0|eng d
020 $a9781461243847
020 $a9780387945088 (ebk.)
020 $a9781461243847
020 $a9780387945088
024 7 $a10.1007/978-1-4612-4384-7$2doi
035 $a(Springer)9781461243847
040 $aSpringer
050 4 $aQA276-280
072 7 $aMAT029000$2bisacsh
072 7 $aPBT$2bicssc
082 04 $a519.5$223
100 1 $aHall, Peter,$eauthor.
245 14 $aThe Bootstrap and Edgeworth Expansion /$cby Peter Hall.
264 1 $aNew York, NY :$bSpringer New York :$bSpringer,$c1992.
300 $aXIII, 353 p.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
490 1 $aSpringer Series in Statistics,$x0172-7397
505 0 $a1: Principles of Bootstrap Methodology -- 2: Principles of Edgeworth Expansion -- 3: An Edgeworth View of the Bootstrap -- 4: Bootstrap Curve Estimation -- 5: Details of Mathematical Rigour -- Appendix I: Number and Sizes of Atoms of Nonparametric Bootstrap Distribution -- Appendix II: Monte Carlo Simulation -- II.1 Introduction -- II.2 Uniform Resampling -- II.3 Linear Approximation -- II.4 Centring Method -- II.5 Balanced Resampling -- II.6 Antithetic Resampling -- II.7 Importance Resampling -- II.7.1 Introduction -- II.7.2 Concept of Importance Resampling -- II.7.3 Importance Resampling for Approximating Bias, Variance, Skewness, etc. -- II.7.4 Importance Resampling for a Distribution Function -- II.8 Quantile Estimation -- Appendix III: Confidence Pictures -- Appendix IV: A Non-Standard Example: Quantite Error Estimation -- IV. 1 Introduction -- IV.2 Definition of the Mean Squared Error Estimate -- IV.3 Convergence Rate of the Mean Squared Error Estimate -- IV.4 Edgeworth Expansions for the Studentized Bootstrap Quantile Estimate -- Appendix V: A Non-Edgeworth View of the Bootstrap -- References -- Author Index.
520 $aThis monograph addresses two quite different topics, in the belief that each can shed light on the other. Firstly, it lays the foundation for a particular view of the bootstrap. Secondly, it gives an account of Edgeworth expansion. Chapter 1 is about the bootstrap, witih almost no mention of Edgeworth expansion; Chapter 2 is about Edgeworth expansion, with scarcely a word about the bootstrap; and Chapters 3 and 4 bring these two themes together, using Edgeworth expansion to explore and develop the properites of the bootstrap. The book is aimed a a graduate level audience who has some exposure to the methods of theoretical statistics. However, technical details are delayed until the last chapter (entitled "Details of Mathematical Rogour"), and so a mathematically able reader without knowledge of the rigorous theory of probability will have no trouble understanding the first four-fifths of the book. The book simultaneously fills two gaps in the literature; it provides a very readable graduate level account of the theory of Edgeworth expansion, and it gives a detailed introduction to the theory of bootstrap methods.
650 10 $aStatistics.
650 0 $aStatistics.
650 24 $aStatistics, general.
776 08 $iPrinted edition:$z9780387945088
830 0 $aSpringer Series in Statistics.
988 $a20140910
906 $0VEN