Applied Mathematics for the Analysis of Biomedical Data
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Applied Mathematics for the Analysis of Biomedical Data
Models, Methods, and MATLAB
Costa, Peter J.
John Wiley & Sons Inc
05/2017
448
Dura
Inglês
9781119269496
15 a 20 dias
738
Descrição não disponível.
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
biomedical data; mathematical models; MATLAB toolbox; data; mathematics; scientific computing; biological systems
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
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
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