Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods
Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.
The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features:
- Over 150 updated figures to clarify theoretical results and to show analyses of real data sets
- An updated presentation of graphic visualization using computer software such as R
- A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering
- More than 130 problems to help readers reinforce the main concepts and ideas presented
- Boxed theorems and results allowing easy identification of crucial ideas
- Figures in color in the digital versions of the book
- A website with related data sets
Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.
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PREFACE TO SECOND EDITION xv
PREFACE TO FIRST EDITION xvii
1 Representation and Geometry of Multivariate Data 1
1.1 Introduction 1
1.2 Historical Perspective 4
1.3 Graphical Display of Multivariate Data Points 5
1.3.1 Multivariate Scatter Diagrams 5
1.3.2 Chernoff Faces 11
1.3.3 Andrews’ Curves and Parallel Coordinate Curves 12
1.3.4 Limitations 14
1.4 Graphical Display of Multivariate Functionals 16
1.4.1 Scatterplot Smoothing by Density Function 16
1.4.2 Scatterplot Smoothing by Regression Function 18
1.4.3 Visualization of Multivariate Functions 19
1.4.3.1 Visualizing Multivariate Regression Functions 24
1.4.4 Overview of Contouring and Surface Display 26
1.5 Geometry of Higher Dimensions 28
1.5.1 Polar Coordinates in d Dimensions 28
1.5.2 Content of Hypersphere 29
1.5.3 Some Interesting Consequences 30
1.5.3.1 Sphere Inscribed in Hypercube 30
1.5.3.2 Hypervolume of a Thin Shell 30
1.5.3.3 Tail Probabilities of Multivariate Normal 31
1.5.3.4 Diagonals in Hyperspace 31
1.5.3.5 Data Aggregate Around Shell 32
1.5.3.6 Nearest Neighbor Distances 32
Problems 33
2 Nonparametric Estimation Criteria 36
2.1 Estimation of the Cumulative Distribution Function 37
2.2 Direct Nonparametric Estimation of the Density 39
2.3 Error Criteria for Density Estimates 40
2.3.1 MISE for Parametric Estimators 42
2.3.1.1 Uniform Density Example 42
2.3.1.2 General Parametric MISE Method with Gaussian Application 43
2.3.2 The L1 Criterion 44
2.3.2.1 L1 versus L2 44
2.3.2.2 Three Useful Properties of the L1 Criterion 44
2.3.3 Data-Based Parametric Estimation Criteria 46
2.4 Nonparametric Families of Distributions 48
2.4.1 Pearson Family of Distributions 48
2.4.2 When Is an Estimator Nonparametric? 49
Problems 50
3 Histograms: Theory and Practice 51
3.1 Sturges’ Rule for Histogram Bin-Width Selection 51
3.2 The L2 Theory of Univariate Histograms 53
3.2.1 Pointwise Mean Squared Error and Consistency 53
3.2.2 Global L2 Histogram Error 56
3.2.3 Normal Density Reference Rule 59
3.2.3.1 Comparison of Bandwidth Rules 59
3.2.3.2 Adjustments for Skewness and Kurtosis 60
3.2.4 Equivalent Sample Sizes 62
3.2.5 Sensitivity of MISE to Bin Width 63
3.2.5.1 Asymptotic Case 63
3.2.5.2 Large-Sample and Small-Sample Simulations 64
3.2.6 Exact MISE versus Asymptotic MISE 65
3.2.6.1 Normal Density 66
3.2.6.2 Lognormal Density 68
3.2.7 Influence of Bin Edge Location on MISE 69
3.2.7.1 General Case 69
3.2.7.2 Boundary Discontinuities in the Density 69
3.2.8 Optimally Adaptive Histogram Meshes 70
3.2.8.1 Bounds on MISE Improvement for Adaptive Histograms 71
3.2.8.2 Some Optimal Meshes 72
3.2.8.3 Null Space of Adaptive Densities 72
3.2.8.4 Percentile Meshes or Adaptive Histograms with Equal Bin Counts 73
3.2.8.5 Using Adaptive Meshes versus Transformation 74
3.2.8.6 Remarks 75
3.3 Practical Data-Based Bin Width Rules 76
3.3.1 Oversmoothed Bin Widths 76
3.3.1.1 Lower Bounds on the Number of Bins 76
3.3.1.2 Upper Bounds on Bin Widths 78
3.3.2 Biased and Unbiased CV 79
3.3.2.1 Biased CV 79
3.3.2.2 Unbiased CV 80
3.3.2.3 End Problems with BCV and UCV 81
3.3.2.4 Applications 81
3.4 L2 Theory for Multivariate Histograms 83
3.4.1 Curse of Dimensionality 85
3.4.2 A Special Case: d = 2 with Nonzero Correlation 87
3.4.3 Optimal Regular Bivariate Meshes 88
3.5 Modes and Bumps in a Histogram 89
3.5.1 Properties of Histogram “Modes” 91
3.5.2 Noise in Optimal Histograms 92
3.5.3 Optimal Histogram Bandwidths for Modes 93
3.5.4 A Useful Bimodal Mixture Density 95
3.6 Other Error Criteria: L1, L4, L6, L8, and L∞ 96
3.6.1 Optimal L1 Histograms 96
3.6.2 Other LP Criteria 97
Problems 97
4 Frequency Polygons 100
4.1 Univariate Frequency Polygons 101
4.1.1 Mean Integrated Squared Error 101
4.1.2 Practical FP Bin Width Rules 104
4.1.3 Optimally Adaptive Meshes 107
4.1.4 Modes and Bumps in a Frequency Polygon 109
4.2 Multivariate Frequency Polygons 110
4.3 Bin Edge Problems 113
4.4 Other Modifications of Histograms 114
4.4.1 Bin Count Adjustments 114
4.4.1.1 Linear Binning 114
4.4.1.2 Adjusting FP Bin Counts to Match Histogram Areas 117
4.4.2 Polynomial Histograms 117
4.4.3 How Much Information Is There in a Few Bins? 120
Problems 122
5 Averaged Shifted Histograms 125
5.1 Construction 126
5.2 Asymptotic Properties 128
5.3 The Limiting ASH as a Kernel Estimator 133
Problems 135
6 Kernel Density Estimators 137
6.1 Motivation for Kernel Estimators 138
6.1.1 Numerical Analysis and Finite Differences 138
6.1.2 Smoothing by Convolution 139
6.1.3 Orthogonal Series Approximations 140
6.2 Theoretical Properties: Univariate Case 142
6.2.1 MISE Analysis 142
6.2.2 Estimation of Derivatives 144
6.2.3 Choice of Kernel 145
6.2.3.1 Higher Order Kernels 145
6.2.3.2 Optimal Kernels 151
6.2.3.3 Equivalent Kernels 153
6.2.3.4 Higher Order Kernels and Kernel Design 155
6.2.3.5 Boundary Kernels 157
6.3 Theoretical Properties: Multivariate Case 161
6.3.1 Product Kernels 162
6.3.2 General Multivariate Kernel MISE 164
6.3.3 Boundary Kernels for Irregular Regions 167
6.4 Generality of the Kernel Method 167
6.4.1 Delta Methods 167
6.4.2 General Kernel Theorem 168
6.4.2.1 Proof of General Kernel Result 168
6.4.2.2 Characterization of a Nonparametric Estimator 169
6.4.2.3 Equivalent Kernels of Parametric Estimators 171
6.5 Cross-Validation 172
6.5.1 Univariate Data 172
6.5.1.1 Early Efforts in Bandwidth Selection 173
6.5.1.2 Oversmoothing 176
6.5.1.3 Unbiased and Biased Cross-Validation 177
6.5.1.4 Bootstrapping Cross-Validation 181
6.5.1.5 Faster Rates and PI Cross-Validation 184
6.5.1.6 Constrained Oversmoothing 187
6.5.2 Multivariate Data 190
6.5.2.1 Multivariate Cross-Validation 190
6.5.2.2 Multivariate Oversmoothing Bandwidths 191
6.5.2.3 Asymptotics of Multivariate Cross-Validation 192
6.6 Adaptive Smoothing 193
6.6.1 Variable Kernel Introduction 193
6.6.2 Univariate Adaptive Smoothing 195
6.6.2.1 Bounds on Improvement 195
6.6.2.2 Nearest-Neighbor Estimators 197
6.6.2.3 Sample-Point Adaptive Estimators 198
6.6.2.4 Data Sharpening 200
6.6.3 Multivariate Adaptive Procedures 202
6.6.3.1 Pointwise Adapting 202
6.6.3.2 Global Adapting 203
6.6.4 Practical Adaptive Algorithms 204
6.6.4.1 Zero-Bias Bandwidths for Tail Estimation 204
6.6.4.2 UCV for Adaptive Estimators 208
6.7 Aspects of Computation 209
6.7.1 Finite Kernel Support and Rounding of Data 210
6.7.2 Convolution and Fourier Transforms 210
6.7.2.1 Application to Kernel Density Estimators 211
6.7.2.2 FFTs 212
6.7.2.3 Discussion 212
6.8 Summary 213
Problems 213
7 The Curse of Dimensionality and Dimension Reduction 217
7.1 Introduction 217
7.2 Curse of Dimensionality 220
7.2.1 Equivalent Sample Sizes 220
7.2.2 Multivariate L1 Kernel Error 222
7.2.3 Examples and Discussion 224
7.3 Dimension Reduction 229
7.3.1 Principal Components 229
7.3.2 Projection Pursuit 231
7.3.3 Informative Components Analysis 234
7.3.4 Model-Based Nonlinear Projection 239
Problems 240
8 Nonparametric Regression and Additive Models 241
8.1 Nonparametric Kernel Regression 242
8.1.1 The Nadaraya–Watson Estimator 242
8.1.2 Local Least-Squares Polynomial Estimators 243
8.1.2.1 Local Constant Fitting 243
8.1.2.2 Local Polynomial Fitting 244
8.1.3 Pointwise Mean Squared Error 244
8.1.4 Bandwidth Selection 247
8.1.5 Adaptive Smoothing 247
8.2 General Linear Nonparametric Estimation 248
8.2.1 Local Polynomial Regression 248
8.2.2 Spline Smoothing 250
8.2.3 Equivalent Kernels 252
8.3 Robustness 253
8.3.1 Resistant Estimators 254
8.3.2 Modal Regression 254
8.3.3 L1 Regression 257
8.4 Regression in Several Dimensions 259
8.4.1 Kernel Smoothing and WARPing 259
8.4.2 Additive Modeling 261
8.4.3 The Curse of Dimensionality 262
8.5 Summary 265
Problems 266
9 Other Applications 267
9.1 Classification, Discrimination, and Likelihood Ratios 267
9.2 Modes and Bump Hunting 273
9.2.1 Confidence Intervals 273
9.2.2 Oversmoothing for Derivatives 275
9.2.3 Critical Bandwidth Testing 275
9.2.4 Clustering via Mixture Models and Modes 277
9.2.4.1 Gaussian Mixture Modeling 277
9.2.4.2 Modes for Clustering 280
9.3 Specialized Topics 286
9.3.1 Bootstrapping 286
9.3.2 Confidence Intervals 287
9.3.3 Survival Analysis 289
9.3.4 High-Dimensional Holes 290
9.3.5 Image Enhancement 292
9.3.6 Nonparametric Inference 292
9.3.7 Final Vignettes 293
9.3.7.1 Principal Curves and Density Ridges 293
9.3.7.2 Time Series Data 294
9.3.7.3 Inverse Problems and Deconvolution 294
9.3.7.4 Densities on the Sphere 294
Problems 294
APPENDIX A Computer Graphics in R 3 296
A.1 Bivariate and Trivariate Contouring Display 296
A.1.1 Bivariate Contouring 296
A.1.2 Trivariate Contouring 299
A.2 Drawing 3-D Objects on the Computer 300
APPENDIX B Data Sets 302
B.1 US Economic Variables Dataset 302
B.2 University Dataset 304
B.3 Blood Fat Concentration Dataset 305
B.4 Penny Thickness Dataset 306
B.5 Gas Meter Accuracy Dataset 307
B.6 Old Faithful Dataset 309
B.7 Silica Dataset 309
B.8 LRL Dataset 310
B.9 Buffalo Snowfall Dataset 310
APPENDIX C Notation and Abbreviations 311
C.1 General Mathematical and Probability Notation 311
C.2 Density Abbreviations 312
C.3 Error Measure Abbreviations 313
C.4 Smoothing Parameter Abbreviations 313
REFERENCES 315
AUTHOR INDEX 334
SUBJECT INDEX 339
Mengenai Pengarang
David W. Scott, Ph D, is Noah Harding Professor in the Department of Statistics at Rice University. The author of over 100 published articles, papers, and book chapters, Dr. Scott is also Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics. He is recipient of the ASA Founder’s Award and the Army Wilks Award. His research interests include computational statistics, data visualization, and density estimation. Dr. Scott is also coeditor of
Wiley Interdisciplinary Reviews: Computational Statistics and previous Editor of the
Journal of Computational and Graphical Statistics.
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