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Table of Contents

 

Preface xi

 

Acknowledgements xiii

 

About the Companion Website xv

 

Introduction xvii

 

1 Data 1

 

1.1 Data Visualization 1

 

1.2 Data Transformations 3

 

1.3 Data Filtering 7

 

1.4 Data Clustering 17

 

1.5 Data Quality and Data Cleaning 25

 

References 28

 

2 Some Examples 29

 

2.1 Glucose–Insulin Interaction 30

 

2.2 Transition from HIV to AIDS 33

 

2.3 Real-Time Polymerase Chain Reaction 37

 

References 45

 

Further Reading 45

 

3 SEIR Models 47

 

3.1 Practical Applications of SEIR Models 50

 

References 88

 

Further Reading 90

 

4 Statistical Pattern Recognition and Classification 93

 

4.1 Measurements and Data Classes 94

 

4.2 Data Preparation Normalization and Weighting Matrix 98

 

4.3 Principal Components 104

 

4.4 Discriminant Analysis 107

 

4.5 Regularized Discriminant Analysis and Classification 112

 

4.6 Minimum Bayes Score Maximum Likelihood and Minimum Bayes Risk 116

 

4.7 The Confusion Matrix Receiver–Operator Characteristic Curves and Assessment Metrics 122

 

4.8 An Example 127

 

4.9 Nonlinear Methods 131

 

References 139

 

Further Reading 140

 

5 Biostatistics and Hypothesis Testing 141

 

5.1 Hypothesis Testing Framework 142

 

5.2 Test of Means 157

 

5.3 Tests of Proportions 179

 

5.4 Tests of Variances 212

 

5.5 Other Hypothesis Tests 232

 

References 268

 

Further Reading 270

 

6 Clustered Data and Analysis of Variance 271

 

6.1 Clustered Matched-Pair Data and Non-Inferiority 273

 

6.2 Clustered Data Assessment Metrics and Diagnostic Likelihood Ratios 278

 

6.3 Relative Diagnostic Likelihood Ratios 286

 

6.4 Analysis of Variance for Clustered Data 291

 

6.5 Examples for Anova 300

 

6.6 Bootstrapping and Confidence Intervals 314

 

References 316

 

Further Reading 316

 

Appendix: Mathematical Matters 317

 

Glossary of MATLAB Functions 335

 

Index 407