This monograph presents an account of the asymptotic behaviour of the weighted bootstrap - a new and powerful statistical technique. Researchers and advanced graduate students studying bootstrap methods will find this a valuable technical survey which is thorough and rigorous. The main aim of this book is to answer two questions: How well does the generalized bootstrap work? What are the differences between all the different weighted schemes? Readers are assumed to have already some familiarity with the bootstrap, but otherwise the account is as self-contained as possible. Proofs are presented in detail, though some lengthy calculations are deferred to appendices.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Table.- I.1) Introduction.- I.2) Some connected works.- I) Asymptotic theory for the generalized bootstrap of statistical differentiate functionals.- I.1) Introduction.- I.2) Fréchet-differentiability and metric indexed by a class of functions.- I.2.1) Differentiability assumptions.- I.2.2) The choice of the metric.- I.2.3) Rate of convergence of the weighted empirical process indexed by a class of functions.- I.3) Consistency of the generalized bootstrapped distribution, variance estimation and Edgeworth expansion.- I.3.1) Consistency of the generalized bootstrapped distribution.- I.3.2) The generalized bootstrap variance estimator.- I.3.3) Edgeworth expansion of the studentized functional.- I.3.4) Inverting Edgeworth expansion to construct confidence intervals.- I.4) Applications.- I.4.1) The mean.- I.4.2) M-estimators.- I.4.3) The probability of being censored.- I.4.4) Multivariate V-statistics.- I.5) Some simulation results.- II) How to choose the weights.- II.1) Introduction.- II.2) Weights generated from an i.i.d. sequence : almost sure results.- II.3) Best weights for the bootstrap of the mean via Edgeworth expansion.- II.3.1) Second order correction.- II.3.2) Coverage probability.- II.4) Choice of the weights for general functional via Edgeworth expansion.- II.4.1) Edgeworth expansion up to o(n-1) for a third order differentiable functional.- II.4.2) Edgeworth Expansion up to o(n-1) for the weighted version.- II.5) Coverage probability for the weighted bootstrap of general functional.- II.5.1) Derivation of the coverage probability.- II.5.2) Choosing the weights via minimization of the coverage probability.- II.5.3) Simulation results.- II.6) Conditional large deviations.- II.7) Conclusion.- III) Some special forms of the weighted bootstrap.- III.1) Introduction.- III.2) Bootstrapping an empirical d.f. when parameters are estimated or under some local alternatives.- III.3) Bootstrap of the extremes and bootstrap of the mean in the infinite variance case.- III.4) Conclusion.- IV) Proofs of results of Chapter I.- IV.1) Proof of Proposition I.2.1.- IV.2) Proof of Proposition I.2.2.- IV.3) Proof of Theorem I.3.1.- IV.4) Some notations and auxilliary lemmas.- IV.5) Proof of Theorem I.3.2.- IV.6) More lemmas to prove Theorem I.3.2.- IV.7) Proof of Theorem I.3.3.- IV.8) Proof of Theorem I.3.4.- IV.9) Proof of Theorem I.3.5.- V) Proofs of results of Chapter II.- V.1) Proofs of results of section II. 2.- V.2) Proof of Formula (II.3.2).- V.3) Proof of Proposition II.4.1.- V.4) Proof of (II.5.6).- V.5) Proof of (II.5.9).- V.6) Proof of (II.5.10).- V.7) Proof of (II.5.11).- V.8) Proof of Theorem II.6.2.- VI) Proofs of results of Chapter III.- VI.1) Proof of Theorem III.1.1.- VI.2) Proof of Theorem III.1.2.- VI.3) Proof of Theorem III.2.1.- VI.4) Proof of Theorem III.2.2.- Appendix 1 : Exchangeable variables of sum 1.- Appendix 5 : Finite sample asymptotic for the mean and the bootstrap mean estimator.- Appendix 6 : Weights giving an almost surely consistent bootstrapped mean.- References.- Notation index.- Author index.
Book by Barbe Philippe Bertail Patrice
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Da: Anybook.com, Lincoln, Regno Unito
Condizione: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,450grams, ISBN:9780387944784. Codice articolo 9543149
Quantità: 1 disponibili
Da: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Condizione: Fine. 230 pp., Paperback, spine faded, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Codice articolo ZB1332765
Quantità: 1 disponibili
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Condizione: New. Codice articolo ABLIING23Feb2215580174090
Quantità: Più di 20 disponibili
Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In. Codice articolo ria9780387944784_new
Quantità: Più di 20 disponibili
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 244 pp. Englisch. Codice articolo 9780387944784
Quantità: 2 disponibili
Da: moluna, Greven, Germania
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 independen. Codice articolo 5912038
Quantità: Più di 20 disponibili
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 244. Codice articolo 263059260
Quantità: 4 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand pp. 244 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam. Codice articolo 5837283
Quantità: 4 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. PRINT ON DEMAND pp. 244. Codice articolo 183059254
Quantità: 4 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Paperback. Condizione: Brand New. 1st edition. 238 pages. 9.50x6.50x0.50 inches. In Stock. Codice articolo x-0387944788
Quantità: 2 disponibili