Statistical learning for biomedical data /:
This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York :
Cambridge University Press,
2011.
|
| Schriftenreihe: | Practical guides to biostatistics and epidemiology.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. "This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research."--Publisher's website |
| Beschreibung: | 1 online resource (xii, 285 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780511993121 0511993129 9780511989308 051198930X 9780511975820 0511975821 1107218802 9781107218802 1282978349 9781282978348 9786612978340 6612978341 0511992092 9780511992094 0511987528 9780511987526 0511991118 9780511991110 |
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| 100 | 1 | |a Malley, James D. |0 http://id.loc.gov/authorities/names/n86037929 | |
| 245 | 1 | 0 | |a Statistical learning for biomedical data / |c James D. Malley, Karen G. Malley, Sinisa Pajevic. |
| 260 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 2011. | ||
| 300 | |a 1 online resource (xii, 285 pages) : |b illustrations | ||
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| 490 | 1 | |a Practical guides to biostatistics and epidemiology | |
| 520 | |a This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a pt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies. | |
| 505 | 0 | 0 | |g Part I. |t Introduction -- |g 1. |t Prologue -- |g 1.1. |t Machines that learn -- some recent history -- |g 1.2. |t Twenty canonical questions -- |g 1.3. |t Outline of the book -- |g 1.4. |t A comment about example datasets -- |g 1.5. |t Software -- |g 2. |t The landscape of learning machines -- |g 2.1. |t Introduction -- |g 2.2. |t Types of data for learning machines -- |g 2.3. |t Will that be supervised or unsupervised? -- |g 2.4. |t An unsupervised example -- |g 2.5. |t More lack of supervision -- where are the parents? -- |g 2.6. |t Engines, complex and primitive -- |g 2.7. |t Model richness means what, exactly? -- |g 2.8. |t Membership or probability of membership? -- |g 2.9. |t A taxonomy of machines? -- |g 2.10. |t A note of caution -- one of many -- |g 2.11. |t Highlights from the theory -- |g 3. |t A mangle of machines -- |g 3.1. |t Introduction -- |g 3.2. |t Linear regression -- |g 3.3. |t Logistic regression -- |g 3.4. |t Linear discriminant -- |g 3.5. |t Bayes classifiers -- regular and naïve -- |g 3.6. |t Logic regression -- |g 3.7. |t k-Nearest neighbors -- |g 3.8. |t Support vector machines -- |g 3.9. |t Neural networks -- |g 3.10. |t Boosting -- |g 3.11. |t Evolutionary and genetic algorithms -- |g 4. |t Three examples and several machines -- |g 4.1. |t Introduction -- |g 4.2. |t Simulated cholesterol data -- |g 4.3. |t Lupus data -- |g 4.4. |t Stroke data -- |g 4.5. |t Biomedical means unbalanced -- |g 4.6. |t Measures of machine performance -- |g 4.7. |t Linear analysis of cholesterol data -- |g 4.8. |t Nonlinear analysis of cholesterol data -- |g 4.9. |t Analysis of the lupus data -- |g 4.10. |t Analysis of the stroke data -- |g 4.11. |t Further analysis of the lupus and stroke data -- |g Part II. |t A machine toolkit -- |g 5. |t Logistic regression -- |g 5.1. |t Introduction -- |g 5.2. |t Inside and around the model -- |g 5.3. |t Interpreting the coefficients -- |g 5.4. |t Using logistic regression as a decision rule -- |g 5.5. |t Logistic regression applied to the cholesterol data -- |g 5.6. |t A cautionary note -- |g 5.7. |t Another cautionary note -- |g 5.8. |t Probability estimates and decision rules -- |g 5.9. |t Evaluating the goodness-of-fit of a logistic regression model -- |g 5.10. |t Calibrating a logistic regression -- |g 5.11. |t Beyond calibration -- |g 5.12. |t Logistic regression and reference models -- |g 6. |t A single decision tree -- |g 6.1. |t Introduction -- |g 6.2. |t Dropping down trees -- |g 6.3. |t Growing a tree -- |g 6.4. |t Selecting features, making splits -- |g 6.5. |t Good split, bad split -- |g 6.6. |t Finding good features for making splits -- |g 6.7. |t Misreading trees -- |g 6.8. |t Stopping and pruning rules -- |g 6.9. |t Using functions of the features -- |g 6.10. |t Unstable trees? -- |g 6.11. |t Variable importance -- growing on trees? -- |g 6.12. |t Permuting for importance -- |g 6.13. |t The continuing mystery of trees -- |g 7. |t Random Forests -- trees everywhere -- |g 7.1. |t Random Forests in less than five minutes -- |g 7.2. |t Random treks through the data -- |g 7.3. |t Random treks through the features -- |g 7.4. |t Walking through the forest -- |g 7.5. |t Weighted and unweighted voting -- |g 7.6. |t Finding subsets in the data using proximities -- |g 7.7. |t Applying Random Forests to the Stroke data -- |g 7.8. |t Random Forests in the universe of machines -- |g Part III. |t Analysis fundamentals -- |g 8. |t Merely two variables -- |g 8.1. |t Introduction -- |g 8.2. |t Understanding correlations -- |g 8.3. |t Hazards of correlations -- |g 8.4. |t Correlations big and small -- |g 9. |t More than two variables -- |g 9.1. |t Introduction -- |g 9.2. |t Tiny problems, large consequences -- |g 9.3. |t Mathematics to the rescue? -- |g 9.4. |t Good models need not be unique -- |g 9.5. |t Contexts and coefficients -- |g 9.6. |t Interpreting and testing coefficients in models -- |g 9.7. |t Merging models, pooling lists, ranking features -- |g 10. |t Resampling methods -- |g 10.1. |t Introduction -- |g 10.2. |t The bootstrap -- |g 10.3. |t When the bootstrap works -- |g 10.4. |t When the bootstrap doesn't work -- |g 10.5. |t Resampling from a single group in different ways -- |g 10.6. |t Resampling from groups with unequal sizes -- |g 10.7. |t Resampling from small datasets -- |g 10.8. |t Permutation methods -- |g 10.9. |t Still more on permutation methods -- |g 11. |t Error analysis and model validation -- |g 11.1. |t Introduction -- |g 11.2. |t Errors? What errors? -- |g 11.3. |t Unbalanced data, unbalanced errors -- |g 11.4. |t Error analysis for a single machine -- |g 11.5. |t Cross-validation error estimation -- |g 11.6. |t Cross-validation or cross-training? -- |g 11.7. |t The leave-one-out method -- |g 11.8. |t The out-of-bag method -- |g 11.9. |t Intervals for error estimates for a single machine -- |g 11.10. |t Tossing random coins into the abyss -- |g 11.11. |t Error estimates for unbalanced data -- |g 11.12. |t Confidence intervals for comparing error values -- |g 11.13. |t Other measures of machine accuracy -- |g 11.14. |t Benchmarking and winning the lottery -- |g 11.15. |t Error analysis for predicting continuous outcomes -- |g Part IV. |t Machine strategies -- |g 12. |t Ensemble methods -- let's take a vote -- |g 12.1. |t Pools of machines -- |g 12.2. |t Weak correlation with outcome can be good enough -- |g 12.3. |t Model averaging -- |g 13. |t Summary and conclusions -- |g 13.1. |t Where have we been? -- |g 13.2. |t So many machines -- |g 13.3. |t Binary decision or probability estimate? -- |g 13.4. |t Survival machines? Risk machines? -- |g 13.5. |t And where are we going? |
| 520 | |a "This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research."--Publisher's website | ||
| 546 | |a English. | ||
| 650 | 0 | |a Medical statistics |x Data processing. | |
| 650 | 0 | |a Biometry |x Data processing. | |
| 650 | 2 | |a Data Interpretation, Statistical | |
| 650 | 2 | |a Models, Statistical | |
| 650 | 6 | |a Biométrie |x Informatique. | |
| 650 | 7 | |a MEDICAL |x Preventive Medicine. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Forensic Medicine. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Public Health. |2 bisacsh | |
| 650 | 7 | |a Biometry |x Data processing |2 fast | |
| 650 | 7 | |a Medical statistics |x Data processing |2 fast | |
| 700 | 1 | |a Malley, Karen G. | |
| 700 | 1 | |a Pajevic, Sinisa. | |
| 758 | |i has work: |a Statistical learning for biomedical data (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGTKhF777xjBtkYdQJMWrC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Malley, James D. |t Statistical learning for biomedical data. |d Cambridge : Cambridge University Press, 2011 |z 9780521875806 |w (DLC) 2011377705 |w (OCoLC)663441381 |
| 830 | 0 | |a Practical guides to biostatistics and epidemiology. |0 http://id.loc.gov/authorities/names/no2006073950 | |
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| author2 | Malley, Karen G. Pajevic, Sinisa |
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| callnumber-raw | QH324.2 .M35 2011eb |
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| contents | pt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies. Introduction -- Prologue -- Machines that learn -- some recent history -- Twenty canonical questions -- Outline of the book -- A comment about example datasets -- Software -- The landscape of learning machines -- Types of data for learning machines -- Will that be supervised or unsupervised? -- An unsupervised example -- More lack of supervision -- where are the parents? -- Engines, complex and primitive -- Model richness means what, exactly? -- Membership or probability of membership? -- A taxonomy of machines? -- A note of caution -- one of many -- Highlights from the theory -- A mangle of machines -- Linear regression -- Logistic regression -- Linear discriminant -- Bayes classifiers -- regular and naïve -- Logic regression -- k-Nearest neighbors -- Support vector machines -- Neural networks -- Boosting -- Evolutionary and genetic algorithms -- Three examples and several machines -- Simulated cholesterol data -- Lupus data -- Stroke data -- Biomedical means unbalanced -- Measures of machine performance -- Linear analysis of cholesterol data -- Nonlinear analysis of cholesterol data -- Analysis of the lupus data -- Analysis of the stroke data -- Further analysis of the lupus and stroke data -- A machine toolkit -- Inside and around the model -- Interpreting the coefficients -- Using logistic regression as a decision rule -- Logistic regression applied to the cholesterol data -- A cautionary note -- Another cautionary note -- Probability estimates and decision rules -- Evaluating the goodness-of-fit of a logistic regression model -- Calibrating a logistic regression -- Beyond calibration -- Logistic regression and reference models -- A single decision tree -- Dropping down trees -- Growing a tree -- Selecting features, making splits -- Good split, bad split -- Finding good features for making splits -- Misreading trees -- Stopping and pruning rules -- Using functions of the features -- Unstable trees? -- Variable importance -- growing on trees? -- Permuting for importance -- The continuing mystery of trees -- Random Forests -- trees everywhere -- Random Forests in less than five minutes -- Random treks through the data -- Random treks through the features -- Walking through the forest -- Weighted and unweighted voting -- Finding subsets in the data using proximities -- Applying Random Forests to the Stroke data -- Random Forests in the universe of machines -- Analysis fundamentals -- Merely two variables -- Understanding correlations -- Hazards of correlations -- Correlations big and small -- More than two variables -- Tiny problems, large consequences -- Mathematics to the rescue? -- Good models need not be unique -- Contexts and coefficients -- Interpreting and testing coefficients in models -- Merging models, pooling lists, ranking features -- Resampling methods -- The bootstrap -- When the bootstrap works -- When the bootstrap doesn't work -- Resampling from a single group in different ways -- Resampling from groups with unequal sizes -- Resampling from small datasets -- Permutation methods -- Still more on permutation methods -- Error analysis and model validation -- Errors? What errors? -- Unbalanced data, unbalanced errors -- Error analysis for a single machine -- Cross-validation error estimation -- Cross-validation or cross-training? -- The leave-one-out method -- The out-of-bag method -- Intervals for error estimates for a single machine -- Tossing random coins into the abyss -- Error estimates for unbalanced data -- Confidence intervals for comparing error values -- Other measures of machine accuracy -- Benchmarking and winning the lottery -- Error analysis for predicting continuous outcomes -- Machine strategies -- Ensemble methods -- let's take a vote -- Pools of machines -- Weak correlation with outcome can be good enough -- Model averaging -- Summary and conclusions -- Where have we been? -- So many machines -- Binary decision or probability estimate? -- Survival machines? Risk machines? -- And where are we going? |
| ctrlnum | (OCoLC)704992965 |
| dewey-full | 614.285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 614 - Forensic medicine; incidence of disease |
| dewey-raw | 614.285 |
| dewey-search | 614.285 |
| dewey-sort | 3614.285 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| format | Electronic eBook |
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Malley, Karen G. Malley, Sinisa Pajevic.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Cambridge ;</subfield><subfield code="a">New York :</subfield><subfield code="b">Cambridge University Press,</subfield><subfield code="c">2011.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xii, 285 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">data file</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Practical guides to biostatistics and epidemiology</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">pt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies.</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="g">Part I.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">1.</subfield><subfield code="t">Prologue --</subfield><subfield code="g">1.1.</subfield><subfield code="t">Machines that learn -- some recent history --</subfield><subfield code="g">1.2.</subfield><subfield code="t">Twenty canonical questions --</subfield><subfield code="g">1.3.</subfield><subfield code="t">Outline of the book --</subfield><subfield code="g">1.4.</subfield><subfield code="t">A comment about example datasets --</subfield><subfield code="g">1.5.</subfield><subfield code="t">Software --</subfield><subfield code="g">2.</subfield><subfield code="t">The landscape of learning machines --</subfield><subfield code="g">2.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">2.2.</subfield><subfield code="t">Types of data for learning machines --</subfield><subfield code="g">2.3.</subfield><subfield code="t">Will that be supervised or unsupervised? --</subfield><subfield code="g">2.4.</subfield><subfield code="t">An unsupervised example --</subfield><subfield code="g">2.5.</subfield><subfield code="t">More lack of supervision -- where are the parents? --</subfield><subfield code="g">2.6.</subfield><subfield code="t">Engines, complex and primitive --</subfield><subfield code="g">2.7.</subfield><subfield code="t">Model richness means what, exactly? --</subfield><subfield code="g">2.8.</subfield><subfield code="t">Membership or probability of membership? --</subfield><subfield code="g">2.9.</subfield><subfield code="t">A taxonomy of machines? --</subfield><subfield code="g">2.10.</subfield><subfield code="t">A note of caution -- one of many --</subfield><subfield code="g">2.11.</subfield><subfield code="t">Highlights from the theory --</subfield><subfield code="g">3.</subfield><subfield code="t">A mangle of machines --</subfield><subfield code="g">3.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">3.2.</subfield><subfield code="t">Linear regression --</subfield><subfield code="g">3.3.</subfield><subfield code="t">Logistic regression --</subfield><subfield code="g">3.4.</subfield><subfield code="t">Linear discriminant --</subfield><subfield code="g">3.5.</subfield><subfield code="t">Bayes classifiers -- regular and naïve --</subfield><subfield code="g">3.6.</subfield><subfield code="t">Logic regression --</subfield><subfield code="g">3.7.</subfield><subfield code="t">k-Nearest neighbors --</subfield><subfield code="g">3.8.</subfield><subfield code="t">Support vector machines --</subfield><subfield code="g">3.9.</subfield><subfield code="t">Neural networks --</subfield><subfield code="g">3.10.</subfield><subfield code="t">Boosting --</subfield><subfield code="g">3.11.</subfield><subfield code="t">Evolutionary and genetic algorithms --</subfield><subfield code="g">4.</subfield><subfield code="t">Three examples and several machines --</subfield><subfield code="g">4.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">4.2.</subfield><subfield code="t">Simulated cholesterol data --</subfield><subfield code="g">4.3.</subfield><subfield code="t">Lupus data --</subfield><subfield code="g">4.4.</subfield><subfield code="t">Stroke data --</subfield><subfield code="g">4.5.</subfield><subfield code="t">Biomedical means unbalanced --</subfield><subfield code="g">4.6.</subfield><subfield code="t">Measures of machine performance --</subfield><subfield code="g">4.7.</subfield><subfield code="t">Linear analysis of cholesterol data --</subfield><subfield code="g">4.8.</subfield><subfield code="t">Nonlinear analysis of cholesterol data --</subfield><subfield code="g">4.9.</subfield><subfield code="t">Analysis of the lupus data --</subfield><subfield code="g">4.10.</subfield><subfield code="t">Analysis of the stroke data --</subfield><subfield code="g">4.11.</subfield><subfield code="t">Further analysis of the lupus and stroke data --</subfield><subfield code="g">Part II.</subfield><subfield code="t">A machine toolkit --</subfield><subfield code="g">5.</subfield><subfield code="t">Logistic regression --</subfield><subfield code="g">5.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">5.2.</subfield><subfield code="t">Inside and around the model --</subfield><subfield code="g">5.3.</subfield><subfield code="t">Interpreting the coefficients --</subfield><subfield code="g">5.4.</subfield><subfield code="t">Using logistic regression as a decision rule --</subfield><subfield code="g">5.5.</subfield><subfield code="t">Logistic regression applied to the cholesterol data --</subfield><subfield code="g">5.6.</subfield><subfield code="t">A cautionary note --</subfield><subfield code="g">5.7.</subfield><subfield code="t">Another cautionary note --</subfield><subfield code="g">5.8.</subfield><subfield code="t">Probability estimates and decision rules --</subfield><subfield code="g">5.9.</subfield><subfield code="t">Evaluating the goodness-of-fit of a logistic regression model --</subfield><subfield code="g">5.10.</subfield><subfield code="t">Calibrating a logistic regression --</subfield><subfield code="g">5.11.</subfield><subfield code="t">Beyond calibration --</subfield><subfield code="g">5.12.</subfield><subfield code="t">Logistic regression and reference models --</subfield><subfield code="g">6.</subfield><subfield code="t">A single decision tree --</subfield><subfield code="g">6.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">6.2.</subfield><subfield code="t">Dropping down trees --</subfield><subfield code="g">6.3.</subfield><subfield code="t">Growing a tree --</subfield><subfield code="g">6.4.</subfield><subfield code="t">Selecting features, making splits --</subfield><subfield code="g">6.5.</subfield><subfield code="t">Good split, bad split --</subfield><subfield code="g">6.6.</subfield><subfield code="t">Finding good features for making splits --</subfield><subfield code="g">6.7.</subfield><subfield code="t">Misreading trees --</subfield><subfield code="g">6.8.</subfield><subfield code="t">Stopping and pruning rules --</subfield><subfield code="g">6.9.</subfield><subfield code="t">Using functions of the features --</subfield><subfield code="g">6.10.</subfield><subfield code="t">Unstable trees? --</subfield><subfield code="g">6.11.</subfield><subfield code="t">Variable importance -- growing on trees? --</subfield><subfield code="g">6.12.</subfield><subfield code="t">Permuting for importance --</subfield><subfield code="g">6.13.</subfield><subfield code="t">The continuing mystery of trees --</subfield><subfield code="g">7.</subfield><subfield code="t">Random Forests -- trees everywhere --</subfield><subfield code="g">7.1.</subfield><subfield code="t">Random Forests in less than five minutes --</subfield><subfield code="g">7.2.</subfield><subfield code="t">Random treks through the data --</subfield><subfield code="g">7.3.</subfield><subfield code="t">Random treks through the features --</subfield><subfield code="g">7.4.</subfield><subfield code="t">Walking through the forest --</subfield><subfield code="g">7.5.</subfield><subfield code="t">Weighted and unweighted voting --</subfield><subfield code="g">7.6.</subfield><subfield code="t">Finding subsets in the data using proximities --</subfield><subfield code="g">7.7.</subfield><subfield code="t">Applying Random Forests to the Stroke data --</subfield><subfield code="g">7.8.</subfield><subfield code="t">Random Forests in the universe of machines --</subfield><subfield code="g">Part III.</subfield><subfield code="t">Analysis fundamentals --</subfield><subfield code="g">8.</subfield><subfield code="t">Merely two variables --</subfield><subfield code="g">8.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">8.2.</subfield><subfield code="t">Understanding correlations --</subfield><subfield code="g">8.3.</subfield><subfield code="t">Hazards of correlations --</subfield><subfield code="g">8.4.</subfield><subfield code="t">Correlations big and small --</subfield><subfield code="g">9.</subfield><subfield code="t">More than two variables --</subfield><subfield code="g">9.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">9.2.</subfield><subfield code="t">Tiny problems, large consequences --</subfield><subfield code="g">9.3.</subfield><subfield code="t">Mathematics to the rescue? --</subfield><subfield code="g">9.4.</subfield><subfield code="t">Good models need not be unique --</subfield><subfield code="g">9.5.</subfield><subfield code="t">Contexts and coefficients --</subfield><subfield code="g">9.6.</subfield><subfield code="t">Interpreting and testing coefficients in models --</subfield><subfield code="g">9.7.</subfield><subfield code="t">Merging models, pooling lists, ranking features --</subfield><subfield code="g">10.</subfield><subfield code="t">Resampling methods --</subfield><subfield code="g">10.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">10.2.</subfield><subfield code="t">The bootstrap --</subfield><subfield code="g">10.3.</subfield><subfield code="t">When the bootstrap works --</subfield><subfield code="g">10.4.</subfield><subfield code="t">When the bootstrap doesn't work --</subfield><subfield code="g">10.5.</subfield><subfield code="t">Resampling from a single group in different ways --</subfield><subfield code="g">10.6.</subfield><subfield code="t">Resampling from groups with unequal sizes --</subfield><subfield code="g">10.7.</subfield><subfield code="t">Resampling from small datasets --</subfield><subfield code="g">10.8.</subfield><subfield code="t">Permutation methods --</subfield><subfield code="g">10.9.</subfield><subfield code="t">Still more on permutation methods 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What errors? --</subfield><subfield code="g">11.3.</subfield><subfield code="t">Unbalanced data, unbalanced errors --</subfield><subfield code="g">11.4.</subfield><subfield code="t">Error analysis for a single machine --</subfield><subfield code="g">11.5.</subfield><subfield code="t">Cross-validation error estimation --</subfield><subfield code="g">11.6.</subfield><subfield code="t">Cross-validation or cross-training? --</subfield><subfield code="g">11.7.</subfield><subfield code="t">The leave-one-out method --</subfield><subfield code="g">11.8.</subfield><subfield code="t">The out-of-bag method --</subfield><subfield code="g">11.9.</subfield><subfield code="t">Intervals for error estimates for a single machine --</subfield><subfield code="g">11.10.</subfield><subfield code="t">Tossing random coins into the abyss --</subfield><subfield code="g">11.11.</subfield><subfield code="t">Error estimates for unbalanced data --</subfield><subfield code="g">11.12.</subfield><subfield code="t">Confidence intervals for comparing error values --</subfield><subfield code="g">11.13.</subfield><subfield code="t">Other measures of machine accuracy --</subfield><subfield code="g">11.14.</subfield><subfield code="t">Benchmarking and winning the lottery --</subfield><subfield code="g">11.15.</subfield><subfield code="t">Error analysis for predicting continuous outcomes --</subfield><subfield code="g">Part IV.</subfield><subfield code="t">Machine strategies --</subfield><subfield code="g">12.</subfield><subfield code="t">Ensemble methods -- let's take a vote --</subfield><subfield code="g">12.1.</subfield><subfield code="t">Pools of machines --</subfield><subfield code="g">12.2.</subfield><subfield code="t">Weak correlation with outcome can be good enough --</subfield><subfield code="g">12.3.</subfield><subfield code="t">Model averaging --</subfield><subfield code="g">13.</subfield><subfield code="t">Summary and conclusions --</subfield><subfield code="g">13.1.</subfield><subfield code="t">Where have we been? --</subfield><subfield code="g">13.2.</subfield><subfield code="t">So many machines --</subfield><subfield code="g">13.3.</subfield><subfield code="t">Binary decision or probability estimate? --</subfield><subfield code="g">13.4.</subfield><subfield code="t">Survival machines? 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Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research."--Publisher's website</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Medical statistics</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Biometry</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Data Interpretation, Statistical</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Models, Statistical</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Biométrie</subfield><subfield code="x">Informatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MEDICAL</subfield><subfield code="x">Preventive Medicine.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MEDICAL</subfield><subfield code="x">Forensic Medicine.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MEDICAL</subfield><subfield code="x">Public Health.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Biometry</subfield><subfield code="x">Data processing</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Medical statistics</subfield><subfield code="x">Data processing</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malley, Karen G.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pajevic, Sinisa.</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Statistical learning for biomedical data (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCGTKhF777xjBtkYdQJMWrC</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Malley, James D.</subfield><subfield code="t">Statistical learning for biomedical data.</subfield><subfield code="d">Cambridge : Cambridge University Press, 2011</subfield><subfield code="z">9780521875806</subfield><subfield code="w">(DLC) 2011377705</subfield><subfield code="w">(OCoLC)663441381</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Practical guides to biostatistics and epidemiology.</subfield><subfield code="0">http://id.loc.gov/authorities/names/no2006073950</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=352491</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">AH22948792</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ebrary</subfield><subfield code="b">EBRY</subfield><subfield code="n">ebr10442834</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">352491</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">3604836</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">3610811</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">3642193</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">9248024</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-ocn704992965 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:43Z |
| institution | BVB |
| isbn | 9780511993121 0511993129 9780511989308 051198930X 9780511975820 0511975821 1107218802 9781107218802 1282978349 9781282978348 9786612978340 6612978341 0511992092 9780511992094 0511987528 9780511987526 0511991118 9780511991110 |
| language | English |
| oclc_num | 704992965 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xii, 285 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Cambridge University Press, |
| record_format | marc |
| series | Practical guides to biostatistics and epidemiology. |
| series2 | Practical guides to biostatistics and epidemiology |
| spelling | Malley, James D. http://id.loc.gov/authorities/names/n86037929 Statistical learning for biomedical data / James D. Malley, Karen G. Malley, Sinisa Pajevic. Cambridge ; New York : Cambridge University Press, 2011. 1 online resource (xii, 285 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Practical guides to biostatistics and epidemiology This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Includes bibliographical references and index. Print version record. pt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies. Part I. Introduction -- 1. Prologue -- 1.1. Machines that learn -- some recent history -- 1.2. Twenty canonical questions -- 1.3. Outline of the book -- 1.4. A comment about example datasets -- 1.5. Software -- 2. The landscape of learning machines -- 2.1. Introduction -- 2.2. Types of data for learning machines -- 2.3. Will that be supervised or unsupervised? -- 2.4. An unsupervised example -- 2.5. More lack of supervision -- where are the parents? -- 2.6. Engines, complex and primitive -- 2.7. Model richness means what, exactly? -- 2.8. Membership or probability of membership? -- 2.9. A taxonomy of machines? -- 2.10. A note of caution -- one of many -- 2.11. Highlights from the theory -- 3. A mangle of machines -- 3.1. Introduction -- 3.2. Linear regression -- 3.3. Logistic regression -- 3.4. Linear discriminant -- 3.5. Bayes classifiers -- regular and naïve -- 3.6. Logic regression -- 3.7. k-Nearest neighbors -- 3.8. Support vector machines -- 3.9. Neural networks -- 3.10. Boosting -- 3.11. Evolutionary and genetic algorithms -- 4. Three examples and several machines -- 4.1. Introduction -- 4.2. Simulated cholesterol data -- 4.3. Lupus data -- 4.4. Stroke data -- 4.5. Biomedical means unbalanced -- 4.6. Measures of machine performance -- 4.7. Linear analysis of cholesterol data -- 4.8. Nonlinear analysis of cholesterol data -- 4.9. Analysis of the lupus data -- 4.10. Analysis of the stroke data -- 4.11. Further analysis of the lupus and stroke data -- Part II. A machine toolkit -- 5. Logistic regression -- 5.1. Introduction -- 5.2. Inside and around the model -- 5.3. Interpreting the coefficients -- 5.4. Using logistic regression as a decision rule -- 5.5. Logistic regression applied to the cholesterol data -- 5.6. A cautionary note -- 5.7. Another cautionary note -- 5.8. Probability estimates and decision rules -- 5.9. Evaluating the goodness-of-fit of a logistic regression model -- 5.10. Calibrating a logistic regression -- 5.11. Beyond calibration -- 5.12. Logistic regression and reference models -- 6. A single decision tree -- 6.1. Introduction -- 6.2. Dropping down trees -- 6.3. Growing a tree -- 6.4. Selecting features, making splits -- 6.5. Good split, bad split -- 6.6. Finding good features for making splits -- 6.7. Misreading trees -- 6.8. Stopping and pruning rules -- 6.9. Using functions of the features -- 6.10. Unstable trees? -- 6.11. Variable importance -- growing on trees? -- 6.12. Permuting for importance -- 6.13. The continuing mystery of trees -- 7. Random Forests -- trees everywhere -- 7.1. Random Forests in less than five minutes -- 7.2. Random treks through the data -- 7.3. Random treks through the features -- 7.4. Walking through the forest -- 7.5. Weighted and unweighted voting -- 7.6. Finding subsets in the data using proximities -- 7.7. Applying Random Forests to the Stroke data -- 7.8. Random Forests in the universe of machines -- Part III. Analysis fundamentals -- 8. Merely two variables -- 8.1. Introduction -- 8.2. Understanding correlations -- 8.3. Hazards of correlations -- 8.4. Correlations big and small -- 9. More than two variables -- 9.1. Introduction -- 9.2. Tiny problems, large consequences -- 9.3. Mathematics to the rescue? -- 9.4. Good models need not be unique -- 9.5. Contexts and coefficients -- 9.6. Interpreting and testing coefficients in models -- 9.7. Merging models, pooling lists, ranking features -- 10. Resampling methods -- 10.1. Introduction -- 10.2. The bootstrap -- 10.3. When the bootstrap works -- 10.4. When the bootstrap doesn't work -- 10.5. Resampling from a single group in different ways -- 10.6. Resampling from groups with unequal sizes -- 10.7. Resampling from small datasets -- 10.8. Permutation methods -- 10.9. Still more on permutation methods -- 11. Error analysis and model validation -- 11.1. Introduction -- 11.2. Errors? What errors? -- 11.3. Unbalanced data, unbalanced errors -- 11.4. Error analysis for a single machine -- 11.5. Cross-validation error estimation -- 11.6. Cross-validation or cross-training? -- 11.7. The leave-one-out method -- 11.8. The out-of-bag method -- 11.9. Intervals for error estimates for a single machine -- 11.10. Tossing random coins into the abyss -- 11.11. Error estimates for unbalanced data -- 11.12. Confidence intervals for comparing error values -- 11.13. Other measures of machine accuracy -- 11.14. Benchmarking and winning the lottery -- 11.15. Error analysis for predicting continuous outcomes -- Part IV. Machine strategies -- 12. Ensemble methods -- let's take a vote -- 12.1. Pools of machines -- 12.2. Weak correlation with outcome can be good enough -- 12.3. Model averaging -- 13. Summary and conclusions -- 13.1. Where have we been? -- 13.2. So many machines -- 13.3. Binary decision or probability estimate? -- 13.4. Survival machines? Risk machines? -- 13.5. And where are we going? "This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research."--Publisher's website English. Medical statistics Data processing. Biometry Data processing. Data Interpretation, Statistical Models, Statistical Biométrie Informatique. MEDICAL Preventive Medicine. bisacsh MEDICAL Forensic Medicine. bisacsh MEDICAL Public Health. bisacsh Biometry Data processing fast Medical statistics Data processing fast Malley, Karen G. Pajevic, Sinisa. has work: Statistical learning for biomedical data (Text) https://id.oclc.org/worldcat/entity/E39PCGTKhF777xjBtkYdQJMWrC https://id.oclc.org/worldcat/ontology/hasWork Print version: Malley, James D. Statistical learning for biomedical data. Cambridge : Cambridge University Press, 2011 9780521875806 (DLC) 2011377705 (OCoLC)663441381 Practical guides to biostatistics and epidemiology. http://id.loc.gov/authorities/names/no2006073950 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=352491 Volltext |
| spellingShingle | Malley, James D. Statistical learning for biomedical data / Practical guides to biostatistics and epidemiology. pt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies. Introduction -- Prologue -- Machines that learn -- some recent history -- Twenty canonical questions -- Outline of the book -- A comment about example datasets -- Software -- The landscape of learning machines -- Types of data for learning machines -- Will that be supervised or unsupervised? -- An unsupervised example -- More lack of supervision -- where are the parents? -- Engines, complex and primitive -- Model richness means what, exactly? -- Membership or probability of membership? -- A taxonomy of machines? -- A note of caution -- one of many -- Highlights from the theory -- A mangle of machines -- Linear regression -- Logistic regression -- Linear discriminant -- Bayes classifiers -- regular and naïve -- Logic regression -- k-Nearest neighbors -- Support vector machines -- Neural networks -- Boosting -- Evolutionary and genetic algorithms -- Three examples and several machines -- Simulated cholesterol data -- Lupus data -- Stroke data -- Biomedical means unbalanced -- Measures of machine performance -- Linear analysis of cholesterol data -- Nonlinear analysis of cholesterol data -- Analysis of the lupus data -- Analysis of the stroke data -- Further analysis of the lupus and stroke data -- A machine toolkit -- Inside and around the model -- Interpreting the coefficients -- Using logistic regression as a decision rule -- Logistic regression applied to the cholesterol data -- A cautionary note -- Another cautionary note -- Probability estimates and decision rules -- Evaluating the goodness-of-fit of a logistic regression model -- Calibrating a logistic regression -- Beyond calibration -- Logistic regression and reference models -- A single decision tree -- Dropping down trees -- Growing a tree -- Selecting features, making splits -- Good split, bad split -- Finding good features for making splits -- Misreading trees -- Stopping and pruning rules -- Using functions of the features -- Unstable trees? -- Variable importance -- growing on trees? -- Permuting for importance -- The continuing mystery of trees -- Random Forests -- trees everywhere -- Random Forests in less than five minutes -- Random treks through the data -- Random treks through the features -- Walking through the forest -- Weighted and unweighted voting -- Finding subsets in the data using proximities -- Applying Random Forests to the Stroke data -- Random Forests in the universe of machines -- Analysis fundamentals -- Merely two variables -- Understanding correlations -- Hazards of correlations -- Correlations big and small -- More than two variables -- Tiny problems, large consequences -- Mathematics to the rescue? -- Good models need not be unique -- Contexts and coefficients -- Interpreting and testing coefficients in models -- Merging models, pooling lists, ranking features -- Resampling methods -- The bootstrap -- When the bootstrap works -- When the bootstrap doesn't work -- Resampling from a single group in different ways -- Resampling from groups with unequal sizes -- Resampling from small datasets -- Permutation methods -- Still more on permutation methods -- Error analysis and model validation -- Errors? What errors? -- Unbalanced data, unbalanced errors -- Error analysis for a single machine -- Cross-validation error estimation -- Cross-validation or cross-training? -- The leave-one-out method -- The out-of-bag method -- Intervals for error estimates for a single machine -- Tossing random coins into the abyss -- Error estimates for unbalanced data -- Confidence intervals for comparing error values -- Other measures of machine accuracy -- Benchmarking and winning the lottery -- Error analysis for predicting continuous outcomes -- Machine strategies -- Ensemble methods -- let's take a vote -- Pools of machines -- Weak correlation with outcome can be good enough -- Model averaging -- Summary and conclusions -- Where have we been? -- So many machines -- Binary decision or probability estimate? -- Survival machines? Risk machines? -- And where are we going? Medical statistics Data processing. Biometry Data processing. Data Interpretation, Statistical Models, Statistical Biométrie Informatique. MEDICAL Preventive Medicine. bisacsh MEDICAL Forensic Medicine. bisacsh MEDICAL Public Health. bisacsh Biometry Data processing fast Medical statistics Data processing fast |
| title | Statistical learning for biomedical data / |
| title_alt | Introduction -- Prologue -- Machines that learn -- some recent history -- Twenty canonical questions -- Outline of the book -- A comment about example datasets -- Software -- The landscape of learning machines -- Types of data for learning machines -- Will that be supervised or unsupervised? -- An unsupervised example -- More lack of supervision -- where are the parents? -- Engines, complex and primitive -- Model richness means what, exactly? -- Membership or probability of membership? -- A taxonomy of machines? -- A note of caution -- one of many -- Highlights from the theory -- A mangle of machines -- Linear regression -- Logistic regression -- Linear discriminant -- Bayes classifiers -- regular and naïve -- Logic regression -- k-Nearest neighbors -- Support vector machines -- Neural networks -- Boosting -- Evolutionary and genetic algorithms -- Three examples and several machines -- Simulated cholesterol data -- Lupus data -- Stroke data -- Biomedical means unbalanced -- Measures of machine performance -- Linear analysis of cholesterol data -- Nonlinear analysis of cholesterol data -- Analysis of the lupus data -- Analysis of the stroke data -- Further analysis of the lupus and stroke data -- A machine toolkit -- Inside and around the model -- Interpreting the coefficients -- Using logistic regression as a decision rule -- Logistic regression applied to the cholesterol data -- A cautionary note -- Another cautionary note -- Probability estimates and decision rules -- Evaluating the goodness-of-fit of a logistic regression model -- Calibrating a logistic regression -- Beyond calibration -- Logistic regression and reference models -- A single decision tree -- Dropping down trees -- Growing a tree -- Selecting features, making splits -- Good split, bad split -- Finding good features for making splits -- Misreading trees -- Stopping and pruning rules -- Using functions of the features -- Unstable trees? -- Variable importance -- growing on trees? -- Permuting for importance -- The continuing mystery of trees -- Random Forests -- trees everywhere -- Random Forests in less than five minutes -- Random treks through the data -- Random treks through the features -- Walking through the forest -- Weighted and unweighted voting -- Finding subsets in the data using proximities -- Applying Random Forests to the Stroke data -- Random Forests in the universe of machines -- Analysis fundamentals -- Merely two variables -- Understanding correlations -- Hazards of correlations -- Correlations big and small -- More than two variables -- Tiny problems, large consequences -- Mathematics to the rescue? -- Good models need not be unique -- Contexts and coefficients -- Interpreting and testing coefficients in models -- Merging models, pooling lists, ranking features -- Resampling methods -- The bootstrap -- When the bootstrap works -- When the bootstrap doesn't work -- Resampling from a single group in different ways -- Resampling from groups with unequal sizes -- Resampling from small datasets -- Permutation methods -- Still more on permutation methods -- Error analysis and model validation -- Errors? What errors? -- Unbalanced data, unbalanced errors -- Error analysis for a single machine -- Cross-validation error estimation -- Cross-validation or cross-training? -- The leave-one-out method -- The out-of-bag method -- Intervals for error estimates for a single machine -- Tossing random coins into the abyss -- Error estimates for unbalanced data -- Confidence intervals for comparing error values -- Other measures of machine accuracy -- Benchmarking and winning the lottery -- Error analysis for predicting continuous outcomes -- Machine strategies -- Ensemble methods -- let's take a vote -- Pools of machines -- Weak correlation with outcome can be good enough -- Model averaging -- Summary and conclusions -- Where have we been? -- So many machines -- Binary decision or probability estimate? -- Survival machines? Risk machines? -- And where are we going? |
| 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 | Medical statistics Data processing. Biometry Data processing. Data Interpretation, Statistical Models, Statistical Biométrie Informatique. MEDICAL Preventive Medicine. bisacsh MEDICAL Forensic Medicine. bisacsh MEDICAL Public Health. bisacsh Biometry Data processing fast Medical statistics Data processing fast |
| topic_facet | Medical statistics Data processing. Biometry Data processing. Data Interpretation, Statistical Models, Statistical Biométrie Informatique. MEDICAL Preventive Medicine. MEDICAL Forensic Medicine. MEDICAL Public Health. Biometry Data processing Medical statistics Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=352491 |
| work_keys_str_mv | AT malleyjamesd statisticallearningforbiomedicaldata AT malleykareng statisticallearningforbiomedicaldata AT pajevicsinisa statisticallearningforbiomedicaldata |