Statistical learning for biomedical data:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2011
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| Schriftenreihe: | Practical guides to biostatistics and epidemiology
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 285 S. Ill., graph. Darst. |
| ISBN: | 9780521699099 0521699096 |
Internformat
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| 100 | 1 | |a Malley, James D. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Statistical learning for biomedical data |c James D. Malley ; Karen G. Malley ; Sinisa Pajevic |
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| 653 | |a Biometry--Data processing. | ||
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Datensatz im Suchindex
| _version_ | 1804145822588207104 |
|---|---|
| adam_text | Titel: Statistical learning for biomedical data
Autor: Malley, James D.
Jahr: 2011
Preface page xi
Acknowledgments xii
Part I Introduction 1
1 Prologue 3
1.1 Machines that learn - some recent history 3
1.2 Twenty canonical questions 7
1.3 Outline of the book 9
1.4 A comment about example datasets 11
1.5 Software 12
Note 13
2 The landscape of learning machines 14
2.1 Introduction 14
2.2 Types of data for learning machines 15
2.3 Will that be supervised or unsupervised? 17
2.4 An unsupervised example 18
2.5 More lack of supervision - where are the parents? 20
2.6 Engines, complex and primitive 20
2.7 Model richness means what, exactly? 22
2.8 Membership or probability of membership? 25
2.9 A taxonomy of machines? 27
2.10 A note of caution - one of many 30
2.11 Highlights from the theory 30
Notes 36
3 A mangle of machines 41
3.1 Introduction 41
3.2 Linear regression 41
3.3 Logistic regression 42
3.4 Linear discriminant 43
3.5 Bayes classifiers - regular and naïve 45
3.6 Logic regression 47
3.7 Ac-Nearest neighbors 48
3.8 Support vector machines 50
3.9 Neural networks 53
3.10 Boosting 54
3.11 Evolutionary and genetic algorithms 55
Notes 56
4 Three examples and several machines 57
4.1 Introduction 57
4.2 Simulated cholesterol data 58
4.3 Lupus data 61
4.4 Stroke data 62
4.5 Biomedical means unbalanced 63
4.6 Measures of machine performance 64
4.7 Linear analysis of cholesterol data 66
4.8 Nonlinear analysis of cholesterol data 67
4.9 Analysis of the lupus data 70
4.10 Analysis of the stroke data 75
4.11 Further analysis of the lupus and stroke data 79
Notes 87
Part II A machine toolkit 89
5 Logistic regression 91
5.1 Introduction 91
5.2 Inside and around the model 92
5.3 Interpreting the coefficients 93
5.4 Using logistic regression as a decision rule 94
5.5 Logistic regression applied to the cholesterol data 94
5.6 A cautionary note 98
5.7 Another cautionary note 101
5.8 Probability estimates and decision rules 102
5.9 Evaluating the goodness-of-fit of a logistic regression model 103
5.10 Calibrating a logistic regression 106
5.11 Beyond calibration 111
5.12 Logistic regression and reference models 113
Notes 115
6 A single decision tree 118
6.1 Introduction 118
6.2 Dropping down trees 118
6.3 Growing a tree 120
6.4 Selecting features, making splits 120
6.5 Good split, bad split 121
6.6 Finding good features for making splits 124
6.7 Misreading trees 125
6.8 Stopping and pruning rules 127
6.9 Using functions of the features 128
6.10 Unstable trees? 129
6.11 Variable importance - growing on trees? 132
6.12 Permuting for importance 134
6.13 The continuing mystery of trees 135
7 Random Forests - trees everywhere 137
7.1 Random Forests in less than five minutes 137
7.2 Random treks through the data 138
7.3 Random treks through the features 139
7.4 Walking through the forest 140
7.5 Weighted and unweighted voting 140
7.6 Finding subsets in the data using proximities 142
7.7 Applying Random Forests to the Stroke data 144
7.8 Random Forests in the universe of machines 151
Notes 153
Part III Analysis fundamentals 155
8 Merely two variables 157
8.1 Introduction 157
8.2 Understanding correlations 158
8.3 Hazards of correlations 159
8.4 Correlations big and small 163
Notes 168
9 More than two variables 171
9.1 Introduction 171
9.2 Tiny problems, large consequences 172
9.3 Mathematics to the rescue? 174
9.4 Good models need not be unique 176
9.5 Contexts and coefficients 179
9.6 Interpreting and testing coefficients in models 181
9.7 Merging models, pooling lists, ranking features 186
Notes 190
10 Resampling methods 198
10.1 Introduction 198
10.2 The bootstrap 198
10.3 When the bootstrap works 201
10.4 When the bootstrap doesn t work 202
10.5 Resampling from a single group in different ways 203
10.6 Resampling from groups with unequal sizes 204
10.7 Resampling from small datasets 206
10.8 Permutation methods 207
10.9 Still more on permutation methods 210
Note 214
11 Error analysis and model validation 215
11.1 Introduction 215
11.2 Errors? What errors? 217
11.3 Unbalanced data, unbalanced errors 218
11.4 Error analysis for a single machine 219
11.5 Cross-validation error estimation 222
11.6 Cross-validation or cross-training? 224
11.7 The leave-one-out method 226
11.8 The out-of-bag method 227
11.9 Intervals for error estimates for a single machine 228
11.10 Tossing random coins into the abyss 230
11.11 Error estimates for unbalanced data 232
11.12 Confidence intervals for comparing error values 233
11.13 Other measures of machine accuracy 236
11.14 Benchmarking and winning the lottery 238
11.15 Error analysis for predicting continuous outcomes 239
Notes 240
Machine strategies 245
Ensemble methods - let s take a vote 247
12.1 Pools of machines 247
12.2 Weak correlation with outcome can be good enough 247
12.3 Model averaging 250
Notes 254
13 Summary and conclusions 255
13.1 Where have we been? 255
13.2 So many machines 257
13.3 Binary decision or probability estimate? 259
13.4 Survival machines? Risk machines? 259
13.5 And where are we going? 260
Appendix 263
References 271
Index 281
The color plate is situated between pages 244 and 245.
|
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| spelling | Malley, James D. Verfasser aut Statistical learning for biomedical data James D. Malley ; Karen G. Malley ; Sinisa Pajevic Cambridge [u.a.] Cambridge Univ. Press 2011 XII, 285 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Practical guides to biostatistics and epidemiology Biostatistik (DE-588)4729990-3 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Epidemiologie (DE-588)4015016-1 gnd rswk-swf Medical statistics--Data processing. Biometry--Data processing. Biostatistik (DE-588)4729990-3 s Medizinische Statistik (DE-588)4127563-9 s Epidemiologie (DE-588)4015016-1 s b DE-604 Malley, Karen G. Verfasser aut Pajevic, Sinisa Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022653322&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Malley, James D. Malley, Karen G. Pajevic, Sinisa Statistical learning for biomedical data Biostatistik (DE-588)4729990-3 gnd Medizinische Statistik (DE-588)4127563-9 gnd Epidemiologie (DE-588)4015016-1 gnd |
| subject_GND | (DE-588)4729990-3 (DE-588)4127563-9 (DE-588)4015016-1 |
| title | Statistical learning for biomedical data |
| title_auth | Statistical learning for biomedical data |
| title_exact_search | Statistical learning for biomedical data |
| title_full | Statistical learning for biomedical data James D. Malley ; Karen G. Malley ; Sinisa Pajevic |
| title_fullStr | Statistical learning for biomedical data James D. Malley ; Karen G. Malley ; Sinisa Pajevic |
| title_full_unstemmed | Statistical learning for biomedical data James D. Malley ; Karen G. Malley ; Sinisa Pajevic |
| title_short | Statistical learning for biomedical data |
| title_sort | statistical learning for biomedical data |
| topic | Biostatistik (DE-588)4729990-3 gnd Medizinische Statistik (DE-588)4127563-9 gnd Epidemiologie (DE-588)4015016-1 gnd |
| topic_facet | Biostatistik Medizinische Statistik Epidemiologie |
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