The Bootstrap And Edgeworth Expansion
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Author | : Peter Hall |
Publisher | : Springer Science & Business Media |
Total Pages | : 359 |
Release | : 2013-12-01 |
Genre | : Mathematics |
ISBN | : 146124384X |
This monograph addresses two quite different topics, each being able to 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. The first two chapters deal with the bootstrap and Edgeworth expansion respectively, while chapters 3 and 4 bring these two themes together, using Edgeworth expansion to explore and develop the properties of the bootstrap. The book is aimed at graduate level for those with some exposure to the methods of theoretical statistics. However, technical details are delayed until the last chapter such that mathematically able readers without knowledge of the rigorous theory of probability will have no trouble understanding most of the book.
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Total Pages | : 352 |
Release | : 1997 |
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Author | : William Cyrus Navidi |
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Total Pages | : 130 |
Release | : 1986 |
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Author | : Roelof Helmers |
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Total Pages | : 15 |
Release | : 1987 |
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Author | : Roelof Helmers |
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Total Pages | : 15 |
Release | : 1987 |
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Author | : Per Aslak Mykland |
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Total Pages | : 242 |
Release | : 1989 |
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Author | : R. Helmers |
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Total Pages | : 15 |
Release | : 1987 |
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Author | : Kani Chen |
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Total Pages | : 88 |
Release | : 1994 |
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Author | : Bradley Efron |
Publisher | : CRC Press |
Total Pages | : 456 |
Release | : 1994-05-15 |
Genre | : Mathematics |
ISBN | : 9780412042317 |
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Author | : Philippe Barbe |
Publisher | : Springer Science & Business Media |
Total Pages | : 236 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461225329 |
INTRODUCTION 1) Introduction In 1979, Efron introduced the bootstrap method as a kind of universal tool to obtain approximation of the distribution of statistics. The now well known underlying idea is the following : consider a sample X of Xl ' n independent and identically distributed H.i.d.) random variables (r. v,'s) with unknown probability measure (p.m.) P . Assume we are interested in approximating the distribution of a statistical functional T(P ) the -1 nn empirical counterpart of the functional T(P) , where P n := n l:i=l aX. is 1 the empirical p.m. Since in some sense P is close to P when n is large, n • • LLd. from P and builds the empirical p.m. if one samples Xl ' ... , Xm n n -1 mn • • P T(P ) conditionally on := mn l: i =1 a • ' then the behaviour of P m n,m n n n X. 1 T(P ) should imitate that of when n and mn get large. n This idea has lead to considerable investigations to see when it is correct, and when it is not. When it is not, one looks if there is any way to adapt it.