Statistics for Imaging, Optics, and Photonics

Informationen zum Autor PETER BAJORSKI, PhD , is Associate Professor in the Graduate Statistics Department at Rochester Institute of Technology, where he is also a core member of the graduate program faculty at the Center for Imaging Science. The author of numerous published articles on statistics and imaging, Dr. Bajorski's areas of statistical expertise include regression techniques, multivariate analysis, design of experiments, nonparametric methods, and visualization methods. A senior member of the IEEE and SPIE, his research in imaging includes unmixing and target detection in spectral images. Klappentext This is the first book fully devoted to the implementation of statistical methods in imaging, optics, and photonics applications with a concentration on statistical inference. The text contains a wide range of relevant statistical methods, including a review of the fundamentals of statistics and expanding into multivariate techniques. The techniques are explained in the context of real examples from various disciplines. It serves as a reference for professionals working in imaging, optics, and photonics; as a textbook for advanced undergraduate and graduate-level courses in multivariate statistics for imaging science, optics, and photonics; and as a supplementary resource in any of the above mentioned courses. Zusammenfassung This is the first book fully devoted to the implementation of statistical methods in imaging, optics, and photonics applications with a concentration on statistical inference. The text contains a wide range of relevant statistical methods, including a review of the fundamentals of statistics and expanding into multivariate techniques. The techniques are explained in the context of real examples from various disciplines. It serves as a reference for professionals working in imaging, optics, and photonics; as a textbook for advanced undergraduate and graduate-level courses in multivariate statistics for imaging science, optics, and photonics; and as a supplementary resource in any of the above mentioned courses. Inhaltsverzeichnis Preface xiii1 Introduction 11.1 Who Should Read This Book, 61.2 How This Book is Organized, 61.3 How to Read This Book and Learn from It, 71.4 Note for Instructors, 81.5 Book Web Site, 92 Fundamentals of Statistics 112.1 Statistical Thinking, 112.2 Data Format, 132.3 Descriptive Statistics, 142.3.1 Measures of Location, 142.3.2 Measures of Variability, 162.4 Data Visualization, 172.4.1 Dot Plots, 172.4.2 Histograms, 192.4.3 Box Plots, 232.4.4 Scatter Plots, 242.5 Probability and Probability Distributions, 262.5.1 Probability and Its Properties, 262.5.2 Probability Distributions, 302.5.3 Expected Value and Moments, 332.5.4 Joint Distributions and Independence, 342.5.5 Covariance and Correlation, 382.6 Rules of Two and Three Sigma, 402.7 Sampling Distributions and the Laws of Large Numbers, 412.8 Skewness and Kurtosis, 443 Statistical Inference 513.1 Introduction, 513.2 Point Estimation of Parameters, 533.2.1 Definition and Properties of Estimators, 533.2.2 The Method of the Moments and Plug-In Principle, 563.2.3 The Maximum Likelihood Estimation, 573.3 Interval Estimation, 603.4 Hypothesis Testing, 633.5 Samples From Two Populations, 713.6 Probability Plots and Testing for Population Distributions, 733.6.1 Probability Plots, 743.6.2 Kolmogorov-Smirnov Statistic, 753.6.3 Chi-Squared Test, 763.6.4 Ryan-Joiner Test for Normality, 763.7 Outlier Detection, 773.8 Monte Carlo Simulations, 793.9 Bootstrap, 794 Statistical Models 854.1 Introduction, 854.2 Regression Models, 854.2.1 Simple Linear Regression Model, 864.2.2 Residual Analysis, 944.2.3 Multiple Linear Regression and Matrix Notation, 964.2.4 Geometric Interpretation in an n-Dimensional Space, 994.2.5 Statistical Inference in Multiple Linear Regression, 1004.2.6 Predicti...

Inhaltsverzeichnis

Preface xiii

1 Introduction 1

1.1 Who Should Read This Book, 6

1.2 How This Book is Organized, 6

1.3 How to Read This Book and Learn from It, 7

1.4 Note for Instructors, 8

1.5 Book Web Site, 9

2 Fundamentals of Statistics 11

2.1 Statistical Thinking, 11

2.2 Data Format, 13

2.3 Descriptive Statistics, 14

2.3.1 Measures of Location, 14

2.3.2 Measures of Variability, 16

2.4 Data Visualization, 17

2.4.1 Dot Plots, 17

2.4.2 Histograms, 19

2.4.3 Box Plots, 23

2.4.4 Scatter Plots, 24

2.5 Probability and Probability Distributions, 26

2.5.1 Probability and Its Properties, 26

2.5.2 Probability Distributions, 30

2.5.3 Expected Value and Moments, 33

2.5.4 Joint Distributions and Independence, 34

2.5.5 Covariance and Correlation, 38

2.6 Rules of Two and Three Sigma, 40

2.7 Sampling Distributions and the Laws of Large Numbers, 41

2.8 Skewness and Kurtosis, 44

3 Statistical Inference 51

3.1 Introduction, 51

3.2 Point Estimation of Parameters, 53

3.2.1 Definition and Properties of Estimators, 53

3.2.2 The Method of the Moments and Plug-In Principle, 56

3.2.3 The Maximum Likelihood Estimation, 57

3.3 Interval Estimation, 60

3.4 Hypothesis Testing, 63

3.5 Samples From Two Populations, 71

3.6 Probability Plots and Testing for Population Distributions, 73

3.6.1 Probability Plots, 74

3.6.2 Kolmogorov-Smirnov Statistic, 75

3.6.3 Chi-Squared Test, 76

3.6.4 Ryan-Joiner Test for Normality, 76

3.7 Outlier Detection, 77

3.8 Monte Carlo Simulations, 79

3.9 Bootstrap, 79

4 Statistical Models 85

4.1 Introduction, 85

4.2 Regression Models, 85

4.2.1 Simple Linear Regression Model, 86

4.2.2 Residual Analysis, 94

4.2.3 Multiple Linear Regression and Matrix Notation, 96

4.2.4 Geometric Interpretation in an n-Dimensional Space, 99

4.2.5 Statistical Inference in Multiple Linear Regression, 100

4.2.6 Prediction of the Response and Estimation of the Mean Response, 104

4.2.7 More on Checking the Model Assumptions, 107

4.2.8 Other Topics in Regression, 110

4.3 Experimental Design and Analysis, 111

4.3.1 Analysis of Designs with Qualitative Factors, 116

4.3.2 Other Topics in Experimental Design, 124

Supplement 4A. Vector and Matrix Algebra, 125

Vectors, 125

Matrices, 127

Eigenvalues and Eigenvectors of Matrices, 130

Spectral Decomposition of Matrices, 130

Positive Definite Matrices, 131

A Square Root Matrix, 131

Supplement 4B. Random Vectors and Matrices, 132

Sphering, 134

5 Fundamentals of Multivariate Statistics 137

5.1 Introduction, 137

5.2 The Multivariate Random Sample, 139

5.3 Multivariate Data Visualization, 143

5.4 The Geometry of the Sample, 148

5.4.1 The Geometric Interpretation of the Sample Mean, 148

5.4.2 The Geometric Interpretation of the Sample Standard Deviation, 149

5.4.3 The Geometric Interpretation of the Sample Correlation Coefficient, 150

5.5 The Generalized Variance, 151

5.6 Distances in the p-Dimensional Space, 159

5.7 The Multivariate Normal (Gaussian) Distribution, 163

5.7.1 The Definition and Properties of the Multivariate Normal Distribution, 163

5.7.2 Properties of the Mahalanobis Distance, 166

6 Multivariate Statistical Inference 173

6.1 Introduction, 173

6.2 Inferences About a Mean Vector, 173

6.2.1 Testing the Multivariate Population Mean, 173

6.2.2 Interval Estimation for the Multivariate Population Mean, 175

6.2.3 T2 Confidence Regions, 179

6.3 Comparing Mean Vectors from Two Populations, 183

6.3.1 Equal Covariance Matrices, 184

6.3.2 Unequal Covariance Matrices and Large Samples, 185

6.3.3 Unequal Covariance Matrices and Samples Sizes Not So Large, 186

6.4 Inferences About a Variance-Covariance Matrix, 187

6.5 How to Check Multivariate Normality, 188

7 Principal Component Analysis 193

7.1 Introduction, 193

7.2 Definition and Properties of Principal Components, 195

7.2.1 Definition of Principal Components, 195

7.2.2 Finding Principal Components, 196

7.2.3 Interpretation of Principal Component Loadings, 200

7.2.4 Scaling of Variables, 207

7.3 Stopping Rules for Principal Component Analysis, 209

7.3.1 Fair-Share Stopping Rules, 210

7.3.2 Large-Gap Stopping Rules, 213

7.4 Principal Component Scores, 217

7.5 Residual Analysis, 220

7.6 Statistical Inference in Principal Component Analysis, 227

7.6.1 Independent and Identically Distributed Observations, 227

7.6.2 Imaging Related Sampling Schemes, 228

7.7 Further Reading, 238

8 Canonical Correlation Analysis 241

8.1 Introduction, 241

8.2 Mathematical Formulation, 242

8.3 Practical Application, 245

8.4 Calculating Variability Explained by Canonical Variables, 246

8.5 Canonical Correlation Regression, 251

8.6 Further Reading, 256

Supplement 8A. Cross-Validation, 256

9 Discrimination and Classification - Supervised Learning 261

9.1 Introduction, 261

9.2 Classification for Two Populations, 264

9.2.1 Classification Rules for Multivariate Normal Distributions, 267

9.2.2 Cross-Validation of Classification Rules, 277

9.2.3 Fisher's Discriminant Function, 280

9.3 Classification for Several Populations, 284

9.3.1 Gaussian Rules, 284

9.3.2 Fisher's Method, 286

9.4 Spatial Smoothing for Classification, 291

9.5 Further Reading, 293

10 Clustering - Unsupervised Learning 297

10.1 Introduction, 297

10.2 Similarity and Dissimilarity Measures, 298

10.2.1 Similarity and Dissimilarity Measures for Observations, 298

10.2.2 Similarity and Dissimilarity Measures for Variables and Other Objects, 304

10.3 Hierarchical Clustering Methods, 304

10.3.1 Single Linkage Algorithm, 305

10.3.2 Complete Linkage Algorithm, 312

10.3.3 Average Linkage Algorithm, 315

10.3.4 Ward Method, 319

10.4 Nonhierarchical Clustering Methods, 320

10.4.1 K-Means Method, 320

10.5 Clustering Variables, 323

10.6 Further Reading, 325

Appendix A Probability Distributions 329

Appendix B Data Sets 349

Appendix C Miscellanea 355

References 365

Index 371

Über den Autor / die Autorin

PETER BAJORSKI, PhD, is Associate Professor in the Graduate Statistics Department at Rochester Institute of Technology, where he is also a core member of the graduate program faculty at the Center for Imaging Science. The author of numerous published articles on statistics and imaging, Dr. Bajorski's areas of statistical expertise include regression techniques, multivariate analysis, design of experiments, nonparametric methods, and visualization methods. A senior member of the IEEE and SPIE, his research in imaging includes unmixing and target detection in spectral images.

Produktdetails

• Autoren: P Bajorski, Peter Bajorski
• Verlag: Wiley, John and Sons Ltd
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 01.10.2011
• EAN: 9780470509456
• ISBN: 978-0-470-50945-6
• Seiten: 416
• Serien: Wiley Series in Probability and Statistics; Wiley Desktop Editions; Wiley Series in Probability an; Wiley Series in Probability and Statistics; Wiley Desktop Editions; Wiley Series in Probability an
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Mathematik > Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Statistik, Physik, Statistics, Physics, Multivariate Analyse, Imaging systems & technology, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Bildgebende Systeme u. Verfahren, Multivariate Analysis, Optik u. Photonik, Optics & Photonics