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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

Aus der Reihe Wiley Series in Probability and Statistics

155,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.09.2022

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

30.09.2022

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 2