01105nam a2200265 i 450099100288481970753620020503174531.0950331s1976 ||| ||| | ger b10428422-39ule_instEXGIL111491ExLBiblioteca InterfacoltàitaGysi, Klaus468424Geschichte der deutschen Literatur :von den Anfangen bis zur Gegenwart /herausgegeben von Klaus Gysi [et al.]3.AuflBerlin :Olk und Wissen Volkseigener,19762 v. ;25 cm.Band 1.: Von den Anfangen bis 1160. - 2 v.Letteratura tedescaStoria.b1042842202-04-1427-06-02991002884819707536LE002 Ted. V C 3/I,11LE002-56502le002-E0.00-l- 00000.i1049775427-06-02LE002 Ted. V C 3/I,2 1LE002-56503le002-E0.00-l- 00000.i1199053314-11-02Geschichte der deutschen Literatur219440UNISALENTOle00201-01-95ma -engxx 0101217nam0 22003011i 450 UON0025626120231205103653.53778-01-30331-820040721d2000 |0itac50 bachiCN|||| 1||||Zhongguo xingshi de wenhua jiexiWnag QuangenBeijingTuanjie chubanshe2000388 p.18 cm001UON002562592001 Zhu zi zhai congshuLetteratura CineseNarrativaSec. XXUONC000084FICNBeijingUONL000457CIN VI BACina - Letteratura moderna e contemporanea - TestiAWUANG QuangenUONV150145689168Tuanjie ChubansheUONV265171650ITSOL20250620RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00256261SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI CIN VI BA 942 N SI SA 111011 7 942 N LETTERATURA PUNJABI - POESIA E TEATROLetteratura Cinese - Narrativa - Sec. XXUONC006913Zhongguo xingshi de wenhua jiexi1234030UNIOR06202nam 22005775 450 991096107520332120250725082052.01-4612-0795-910.1007/978-1-4612-0795-5(CKB)3400000000089272(SSID)ssj0001297317(PQKBManifestationID)11724812(PQKBTitleCode)TC0001297317(PQKBWorkID)11362577(PQKB)11024583(DE-He213)978-1-4612-0795-5(MiAaPQ)EBC3073974(PPN)238006492(EXLCZ)99340000000008927220121227d1995 u| 0engurnn|008mamaatxtccrThe Jackknife and Bootstrap /by Jun Shao, Dongsheng Tu1st ed. 1995.New York, NY :Springer New York :Imprint: Springer,1995.1 online resource (XVII, 517 p.) Springer Series in Statistics,2197-568XBibliographic Level Mode of Issuance: Monograph0-387-94515-6 1-4612-6903-2 Includes bibliographical references and index.1. Introduction -- 1.1 Statistics and Their Sampling Distributions -- 1.2 The Traditional Approach -- 1.3 The Jackknife -- 1.4 The Bootstrap -- 1.5 Extensions to Complex Problems -- 1.6 Scope of Our Studies -- 2. Theory for the Jackknife -- 2.1 Variance Estimation for Functions of Means -- 2.2 Variance Estimation for Functionals -- 2.3 The Delete-d Jackknife -- 2.4 Other Applications -- 2.5 Conclusions and Discussions -- 3. Theory for the Bootstrap -- 3.1 Techniques in Proving Consistency -- 3.2 Consistency: Some Major Results -- 3.3 Accuracy and Asymptotic Comparisons -- 3.4 Fixed Sample Performance -- 3.5 Smoothed Bootstrap -- 3.6 Nonregular Cases -- 3.7 Conclusions and Discussions -- 4. Bootstrap Confidence Sets and Hypothesis Tests -- 4.1 Bootstrap Confidence Sets -- 4.2 Asymptotic Theory -- 4.3 The Iterative Bootstrap and Other Methods -- 4.4 Empirical Comparisons -- 4.5 Bootstrap Hypothesis Tests -- 4.6 Conclusions and Discussions -- 5. Computational Methods -- 5.1 The Delete-1 Jackknife -- 5.2 The Delete-d Jackknife -- 5.3 Analytic Approaches for the Bootstrap -- 5.4 Simulation Approaches for the Bootstrap -- 5.5 Conclusions and Discussions -- 6. Applications to Sample Surveys -- 6.1 Sampling Designs and Estimates -- 6.2 Resampling Methods -- 6.3 Comparisons by Simulation -- 6.4 Asymptotic Results -- 6.5 Resampling Under Imputation -- 6.6 Conclusions and Discussions -- 7. Applications to Linear Models -- 7.1 Linear Models and Regression Estimates -- 7.2 Variance and Bias Estimation -- 7.3 Inference and Prediction Using the Bootstrap -- 7.4 Model Selection -- 7.5 Asymptotic Theory -- 7.6 Conclusions and Discussions -- 8. Applications to Nonlinear, Nonparametric, and Multivariate Models -- 8.1 Nonlinear Regression -- 8.2 Generalized Linear Models -- 8.3 Cox’s Regression Models -- 8.4 Kernel Density Estimation.-8.5 Nonparametric Regression -- 8.6 Multivariate Analysis -- 8.7 Conclusions and Discussions -- 9. Applications to Time Series and Other Dependent Data -- 9.1 m-Dependent Data -- 9.2 Markov Chains -- 9.3 Autoregressive Time Series -- 9.4 Other Time Series -- 9.5 Stationary Processes -- 9.6 Conclusions and Discussions -- 10. Bayesian Bootstrap and Random Weighting -- 10.1 Bayesian Bootstrap -- 10.2 Random Weighting -- 10.3 Random Weighting for Functional and Linear Models -- 10.4 Empirical Results for Random Weighting -- 10.5 Conclusions and Discussions -- Appendix A. Asymptotic Results -- A.1 Modes of Convergence -- A.2 Convergence of Transformations -- A.4 The Borel-Cantelli Lemma -- A.5 The Law of Large Numbers -- A.6 The Law of the Iterated Logarithm -- A.7 Uniform Integrability -- A.8 The Central Limit Theorem -- A.9 The Berry-Esséen Theorem -- A.10 Edgeworth Expansions -- A.11 Cornish-Fisher Expansions -- Appendix B. Notation -- References -- Author Index.The jackknife and bootstrap are the most popular data-resampling meth­ ods used in statistical analysis. The resampling methods replace theoreti­ cal derivations required in applying traditional methods (such as substitu­ tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel­ opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample properties of the jackknife and bootstrap are mostly investigated by examples and/or empirical simulation studies. In addition to the theory for the jackknife and bootstrap methods in problems with independent and identically distributed (Li.d.) data, we try to cover, as much as we can, the applications of the jackknife and bootstrap in various complicated non-Li.d. data problems.Springer Series in Statistics,2197-568XMathematicsApplications of MathematicsMathematics.Applications of Mathematics.519Shao Junauthttp://id.loc.gov/vocabulary/relators/aut351292Tu Dongshengauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910961075203321The Jackknife and Bootstrap4412636UNINA01632nam0 22003853i 450 TSA001400620251003044415.0027308799120071219d1989 ||||0itac50 baenggbz01i xxxe z01nReconfigurable processor-arraya bit-sliced parallel computerAndrew RushtonLondonPitmanCambridge (Mass.)The MIT press1989175 p.25 cm.Research monographs in parallel and distributed computing001MIL01346242001 Research monographs in parallel and distributed computingElaborazione parallela dei datiFIRCFIC081093IElaborazione distribuitaFIRCFIC243635NMicroprocessoriFIRCFIC027016E004ELABORAZIONE DEI DATI. SCIENZA DEGLI ELABORATORI. INFORMATICA14004.35Modi di elaborazione. Multielaborazione22Grid computingElaborazione distribuita dei datiElaborazione distribuitaGrid computingElaborazione distribuitaElaborazione distribuita dei datiRushton, AndrewTSAV007262070771816ITIT-00000020071219IT-BN0095 NAP 01SALA DING $TSA0014006Biblioteca Centralizzata di Ateneo1 v. 01SALA DING 004 RUS.re 0102 0000009705 VMA A4 1 v.Y 2007121920071219 01Reconfigurable processor-array1575332UNISANNIO