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  • Produktbild: Nonparametric Statistics with Applications to Science and Engineering with R
  • Produktbild: Nonparametric Statistics with Applications to Science and Engineering with R

Nonparametric Statistics with Applications to Science and Engineering with R Engineering With

Aus der Reihe Wiley Series in Probability and Statistics

155,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

18.10.2022

Verlag

John Wiley & Sons

Seitenzahl

448

Maße (L/B/H)

23,5/15,7/2,8 cm

Gewicht

794 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-119-26813-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

18.10.2022

Verlag

John Wiley & Sons

Seitenzahl

448

Maße (L/B/H)

23,5/15,7/2,8 cm

Gewicht

794 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-119-26813-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Nonparametric Statistics with Applications to Science and Engineering with R
  • Produktbild: Nonparametric Statistics with Applications to Science and Engineering with R

  • Preface xi

    1 Introduction 1

    1.1 Efficiency of Nonparametric Methods 2

    1.2 Overconfidence Bias 4

    1.3 Computing with R 5

    1.4 Exercises 6

    References 7

    2 Probability Basics 9

    2.1 Helpful Functions 10

    2.2 Events, Probabilities and Random Variables 12

    2.3 Numerical Characteristics of Random Variables 13

    2.4 Discrete Distributions 14

    2.5 Continuous Distributions 18

    2.6 Mixture Distributions 24

    2.7 Exponential Family of Distributions 26

    2.8 Stochastic Inequalities 26

    2.9 Convergence of Random Variables 28

    2.10 Exercises 32

    References 34

    3 Statistics Basics 35

    3.1 Estimation 36

    3.2 Empirical Distribution Function 36

    3.3 Statistical Tests 38

    3.4 Confidence Intervals 41

    3.5 Likelihood 45

    3.6 Exercises 49

    References 51

    4 Bayesian Statistics 53

    4.1 The Bayesian Paradigm 53

    4.2 Ingredients for Bayesian Inference 54

    4.3 Point Estimation 58

    4.4 Interval Estimation: Credible Sets 60

    4.5 Bayesian Testing 62

    4.6 Bayesian Prediction 65

    4.7 Bayesian Computation and Use of WinBUGS 67

    4.8 Exercises 69

    References 73

    5 Order Statistics 75

    5.1 Joint Distributions of Order Statistics 77

    5.2 Sample Quantiles 79

    5.3 Tolerance Intervals 79

    5.4 Asymptotic Distributions of Order Statistics 81

    5.5 Extreme Value Theory 82

    5.6 Ranked Set Sampling 83

    5.7 Exercises 84

    References 87

    6 Goodness of Fit 89

    6.1 KolmogorovSmirnov Test Statistic 90

    6.2 Smirnov Test to Compare Two Distributions 96

    6.3 Specialized Tests 99

    6.4 Probability Plotting 106

    6.5 Runs Test 112

    6.6 Meta Analysis 117

    6.7 Exercises 121

    References 125

    7 Rank Tests 127

    7.1 Properties of Ranks 128

    7.2 Sign Test 130

    7.3 Spearman Coefficient of Rank Correlation 135

    7.4 Wilcoxon Signed Rank Test 139

    7.5 Wilcoxon (TwoSample) Sum Rank Test 142

    7.6 MannWhitney U Test 144

    7.7 Test of Variances 146

    7.8 Walsh Test for Outliers 147

    7.9 Exercises 148

    References 153

    8 Designed Experiments 155

    8.1 KruskalWallis Test 156

    8.2 Friedman Test 160

    8.3 Variance Test for Several Populations 165

    8.4 Exercises 166

    References 169

    9 Categorical Data 171

    9.1 ChiSquare and GoodnessofFit 172

    9.2 Contingency Tables 178

    9.3 Fisher Exact Test 183

    9.4 Mc Nemar Test 184

    9.5 Cochran's Test 186

    9.6 MantelHaenszel Test 188

    9.7 CLT for Multinomial Probabilities 190

    9.8 Simpson's Paradox 191

    9.9 Exercises 193

    References 200

    10 Estimating Distribution Functions 203

    10.1 Introduction 203

    10.2 Nonparametric Maximum Likelihood 204

    10.3 KaplanMeier Estimator 205

    10.4 Confidence Interval for F 213

    10.5 Plugin Principle 214

    10.6 SemiParametric Inference 215

    10.7 Empirical Processes 217

    10.8 Empirical Likelihood 218

    10.9 Exercises 221

    References 223

    11 Density Estimation 225

    11.1 Histogram 226

    11.2 Kernel and Bandwidth 228

    11.3 Exercises 235

    References 236

    12 Beyond Linear Regression 237

    12.1 Least Squares Regression 238

    12.2 Rank Regression 239

    12.3 Robust Regression 243

    12.4 Isotonic Regression 249

    12.5 Generalized Linear Models 252

    12.6 Exercises 259

    References 261

    13 Curve Fitting Techniques 263

    13.1 Kernel Estimators 265

    13.2 Nearest Neighbor Methods 269

    13.3 Variance Estimation 272

    13.4 Splines 273

    13.5 Summary 279

    13.6 Exercises 279

    References 282

    14 Wavelets 285

    14.1 Introduction to Wavelets 285

    14.2 How Do the Wavelets Work? 288

    14.3 Wavelet Shrinkage 295

    14.4 Exercises 304

    References 305

    15 Bootstrap 307

    15.1 Bootstrap Sampling 307

    15.2 Nonparametric Bootstrap 309

    15.3 Bias Correction for Nonparametric Intervals 315

    15.4 The Jackknife 317

    15.5 Bayesian Bootstrap 318

    15.6 Permutation Tests 320

    15.7 More on the Bootstrap 324

    15.8 Exercises 325

    References 327

    16 EM Algorithm 329

    16.1 Fisher's Example 331

    16.2 Mixtures 333

    16.3 EM and Order Statistics 338

    16.4 MAP via EM 339

    16.5 Infection Pattern Estimation 341

    16.6 Exercises 342

    References 343

    17 Statistical Learning 345

    17.1 Discriminant Analysis 346

    17.2 Linear Classification Models 349

    17.3 Nearest Neighbor Classification 353

    17.4 Neural Networks 355

    17.5 Binary Classification Trees 361

    17.6 Exercises 368

    References 369

    18 Nonparametric Bayes 371

    18.1 Dirichlet Processes 372

    18.2 Bayesian Categorical Models 380

    18.3 Infinitely Dimensional Problems 383

    18.4 Exercises 387

    References 389

    A WinBUGS 392

    A.1 Using WinBUGS 393

    A.2 Builtin

    Functions 396

    B R Coding 400

    B.1 Programming in R 400

    B.2 Basics of R 402

    B.3 R Commands 403

    B.4 R for Statistics 405

    R Index 411

    Author Index 414

    Subject Index 418