LEADER 01176nam a2200253 i 4500 001 991004134309707536 008 090206s2006 it b 101 0 ita d 020 $a9788883980428 035 $ab13808953-39ule_inst 040 $aDip.to Studi Giuridici$bita 082 0 $a940.3072 245 02$aL'inizio della fine :$bla Prima Guerra Mondiale e le sue conseguenze sulla storia d'Europa tra pensiero politico, istituzioni e cultura /$ca cura di Giorgio La Rosa 260 $aFirenze :$bEuropean Press Academic Publishing,$c2006 300 $a278 p. ;$c21 cm 500 $aSaggi sviluppati a seguito di una giornata di studi tenuta nel 2004 presso la Facoltą di Giurisprudenza dell'Universitą degli Studi dell'Insubria (Como) sul tema della Grande Guerra 504 $aBibliografia: p. [35]-42 650 04$aGuerra mondiale 1914-1918 700 1 $aLa Rosa, Giorgio 907 $a.b13808953$b02-04-14$c06-02-09 912 $a991004134309707536 945 $aLE027 940.30 LAR01.01$g1$i2027000225873$lle027$o-$pE22.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i15014083$z14-10-09 996 $aInizio della fine$9831167 997 $aUNISALENTO 998 $ale027$b06-02-09$cm$da $e-$fita$git $h2$i0 LEADER 05635nam 2200733 450 001 9910789481603321 005 20231110224846.0 010 $a1-118-62541-2 010 $a1-118-62545-5 035 $a(CKB)3460000000120735 035 $a(EBL)1771575 035 $a(SSID)ssj0000719792 035 $a(PQKBManifestationID)12297401 035 $a(PQKBTitleCode)TC0000719792 035 $a(PQKBWorkID)10660251 035 $a(PQKB)10015253 035 $a(Au-PeEL)EBL1771575 035 $a(CaPaEBR)ebr10915823 035 $a(CaONFJC)MIL639080 035 $a(OCoLC)889674830 035 $a(Au-PeEL)EBL7103888 035 $a(CaSebORM)9780470467046 035 $a(MiAaPQ)EBC1771575 035 $a(MiAaPQ)EBC7103888 035 $a(EXLCZ)993460000000120735 100 $a20140902h20112011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn introduction to bootstrap methods with applications to R /$fMichael R. Chernick, Robert A. LaBudde 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2011. 210 4$d2011 215 $a1 online resource (236 p.) 225 1 $aNew York Academy of Sciences 300 $aDescription based upon print version of record. 311 $a1-322-07829-7 311 $a0-470-46704-5 320 $aIncludes bibliographical references and index. 327 $aCover ; Title Page ; Copyright ; Contents ; Preface ; Acknowledgments ; List of Tables ; 1: INTRODUCTION ; 1.1 Historical Background ; 1.2 Definition and Relationship to the Delta Method and Other Resampling Methods ; 1.2.1 Jackknife ; 1.2.2 Delta Method ; 1.2.3 Cross Validation ; 1.2.4 Subsampling ; 1.3 Wide Range of Applications ; 1.4 The Bootstrap and the R Language System ; 1.5 Historical Notes ; 1.6 Exercises ; References ; 2: ESTIMATION; 2.1 Estimating Bias ; 2.1.1 Bootstrap Adjustment ; 2.1.2 Error Rate Estimation in Discriminant Analysis 327 $a2.1.3 Simple Example of Linear Discrimination and Bootstrap Error Rate Estimation 2.1.4 Patch Data Example ; 2.2 Estimating Location ; 2.2.1 Estimating a Mean ; 2.2.2 Estimating a Median ; 2.3 Estimating Dispersion ; 2.3.1 Estimating an Estimate's Standard Error ; 2.3.2 Estimating Interquartile Range ; 2.4 Linear Regression ; 2.4.1 Overview ; 2.4.2 Bootstrapping Residuals ; 2.4.3 Bootstrapping Pairs (response and Predictor Vector) ; 2.4.4 Heteroscedasticity of Variance: the Wild Bootstrap ; 2.4.5 a Special Class of Linear Regression Models: Multivariable Fractional Polynomials 327 $a2.5 Nonlinear Regression 2.5.1 Examples of Nonlinear Models ; 2.5.2 a Quasi Optical Experiment ; 2.6 Nonparametric Regression ; 2.6.1 Examples of Nonparametric Regression Models ; 2.6.2 Bootstrap Bagging ; 2.7 Historical Notes ; 2.8 Exercises ; References ; 3: CONFIDENCE INTERVALS ; 3.1 Subsampling, Typical Value Theorem, and Efron's Percentile Method ; 3.2 Bootstrap-t ; 3.3 Iterated Bootstrap ; 3.4 Bias Corrected (BC) Bootstrap ; 3.5 Bca and Abc ; 3.6 Tilted Bootstrap ; 3.7 Variance Estimation with Small Sample Sizes ; 3.8 Historical Notes ; 3.9 Exercises ; References ; 4: HYPOTHESIS TESTING 327 $a4.1 Relationship to Confidence Intervals 4.2 Why Test Hypotheses Differently? ; 4.3 Tendril Dx Example ; 4.4 Klingenberg Example: Binary Dose-response ; 4.5 Historical Notes ; 4.6 Exercises ; References ; 5: TIME SERIES; 5.1 Forecasting Methods ; 5.2 Time Domain Models ; 5.3 Can Bootstrapping Improve Prediction Intervals? ; 5.4 Model Based Methods ; 5.4.1 Bootstrapping Stationary Autoregressive Processes ; 5.4.2 Bootstrapping Explosive Autoregressive Processes ; 5.4.3 Bootstrapping Unstable Autoregressive Processes ; 5.4.4 Bootstrapping Stationary Arma Processes 327 $a5.5 Block Bootstrapping for Stationary Time Series 5.6 Dependent Wild Bootstrap (DWB) ; 5.7 Frequency-based Approaches for Stationary Time Series ; 5.8 Sieve Bootstrap ; 5.9 Historical Notes ; 5.10 Exercises ; References ; 6: BOOTSTRAP VARIANTS; 6.1 Bayesian Bootstrap ; 6.2 Smoothed Bootstrap ; 6.3 Parametric Bootstrap ; 6.4 Double Bootstrap ; 6.5 the M-out-of-n Bootstrap ; 6.6 the Wild Bootstrap ; 6.7 Historical Notes ; 6.8 Exercise ; References ; 7: CHAPTER SPECIAL TOPICS; 7.1 Spatial Data ; 7.1.1 Kriging ; 7.1.2 Asymptotics for Spatial Data ; 7.1.3 Block Bootstrap on Regular Grids 327 $a7.1.4 Block Bootstrap on Irregular Grids 330 $aA comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. 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