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 Wave
