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Produktbild: The Weighted Bootstrap
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The Weighted Bootstrap

Aus der Reihe Lecture Notes in Statistics

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.02.1995

Verlag

Springer Us

Seitenzahl

230

Maße (L/B/H)

23,5/15,5/1,3 cm

Gewicht

376 g

Auflage

Softcover reprint of the original 1st ed. 1995

Sprache

Englisch

ISBN

978-0-387-94478-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.02.1995

Verlag

Springer Us

Seitenzahl

230

Maße (L/B/H)

23,5/15,5/1,3 cm

Gewicht

376 g

Auflage

Softcover reprint of the original 1st ed. 1995

Sprache

Englisch

ISBN

978-0-387-94478-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: The Weighted Bootstrap
  • 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.