Analyzing the large numbers of variables in biomedical and satellite imagery

Machine generated contents note:1.Very Large Arrays -- 1.1.Applications -- 1.2.Problems -- 1.3.Solutions -- 2.Permutation Tests -- 2.1.Two-Sample Comparison -- 2.1.1.Blocks -- 2.2.k-Sample Comparison -- 2.3.Computing The p-Value -- 2.3.1.Monte Carlo Method -- 2.3.2.An R Program -- 2.4.Multiple-Varia...

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Bibliographische Detailangaben
1. Verfasser:	Good, Phillip I. (VerfasserIn)
Format:	UnknownFormat
Sprache:	eng
Veröffentlicht:	Hoboken, NJ Wiley c2011
Schlagworte:	Data mining > Mathematical statistics > Biomedical engineering > Data processing > Remote sensing > Functions of several complex variables > Data Mining > Diagnostic Imaging > Models, Statistical > Oligonucleotide Array Sequence Analysis > Biostatistik > Medizinische Statistik
Online Zugang:	Inhaltsverzeichnis
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Beschreibung
Zusammenfassung:	Machine generated contents note:1.Very Large Arrays -- 1.1.Applications -- 1.2.Problems -- 1.3.Solutions -- 2.Permutation Tests -- 2.1.Two-Sample Comparison -- 2.1.1.Blocks -- 2.2.k-Sample Comparison -- 2.3.Computing The p-Value -- 2.3.1.Monte Carlo Method -- 2.3.2.An R Program -- 2.4.Multiple-Variable Comparisons -- 2.4.1.Euclidean Distance Matrix Analysis -- 2.4.2.Hotelling's T2 -- 2.4.3.Mantel's U -- 2.4.4.Combining Univariate Tests -- 2.4.5.Gene Set Enrichment Analysis -- 2.5.Categorical Data -- 2.6.Software -- 2.7.Summary -- 3.Applying the Permutation Test -- 3.1.Which Variables Should Be Included? -- 3.2.Single-Value Test Statistics -- 3.2.1.Categorical Data -- 3.2.2.A Multivariate Comparison Based on a Summary Statistic -- 3.2.3.A Multivariate Comparison Based on Variants of Hotelling's T2 3.2.4.Adjusting for Covariates -- 3.2.5.Pre-Post Comparisons -- 3.2.6.Choosing a Statistic: Time-Course Microarrays -- 3.3.Recommended Approaches -- 3.4.To Learn More -- 4.Biological Background -- 4.1.Medical Imaging -- 4.1.1.Ultrasound -- 4.1.2.EEG/MEG -- 4.1.3.Magnetic Resonance Imaging -- 4.1.3.1.MRI -- 4.1.3.2.fMRI -- 4.1.4.Positron Emission Tomography -- 4.2.Microarrays -- 4.3.To Learn More -- 5.Multiple Tests -- 5.1.Reducing the Number of Hypotheses to Be Tested -- 5.1.1.Normalization -- 5.1.2.Selection Methods -- 5.1.2.1.Univariate Statistics -- 5.1.2.2.Which Statistic? -- 5.1.2.3.Heuristic Methods -- 5.1.2.4.Which Method? -- 5.2.Controlling the Over All Error Rate -- 5.2.1.An Example: Analyzing Data from Microarrays -- 5.3.Controlling the False Discovery Rate -- 5.3.1.An Example: Analyzing Time-Course Data from Microarrays -- 5.4.Gene Set Enrichment Analysis 5.5.Software for Performing Multiple Simultaneous Tests -- 5.5.1.AFNI -- 5.5.2.Cyber-T -- 5.5.3.dChip -- 5.5.4.ExactFDR -- 5.5.5.GESS -- 5.5.6.HaploView -- 5.5.7.MatLab -- 5.5.8.R -- 5.5.9.SAM -- 5.5.10.ParaSam -- 5.6.Summary -- 5.7.To Learn More -- 6.The Bootstrap -- 6.1.Samples and Populations -- 6.2.Precision of an Estimate -- 6.2.1.R Code -- 6.2.2.Applying the Bootstrap -- 6.2.3.Bootstrap Reproducibility Index -- 6.2.4.Estimation in Regression Models -- 6.3.Confidence Intervals -- 6.3.1.Testing for Equivalence -- 6.3.2.Parametric Bootstrap -- 6.3.3.Blocked Bootstrap -- 6.3.4.Balanced Bootstrap -- 6.3.5.Adjusted Bootstrap -- 6.3.6.Which Test? -- 6.4.Determining Sample Size -- 6.4.1.Establish a Threshold -- 6.5.Validation -- 6.5.1.Cluster Analysis -- 6.5.2.Correspondence Analysis -- 6.6.Building a Model -- 6.7.How Large Should The Samples Be? 6.8.Summary -- 6.9.To Learn More -- 7.Classification Methods -- 7.1.Nearest Neighbor Methods -- 7.2.Discriminant Analysis -- 7.3.Logistic Regression -- 7.4.Principal Components -- 7.5.Naive Bayes Classifier -- 7.6.Heuristic Methods -- 7.7.Decision Trees -- 7.7.1.A Worked-Through Example -- 7.8.Which Algorithm Is Best for Your Application? -- 7.8.1.Some Further Comparisons -- 7.8.2.Validation Versus Cross-validation -- 7.9.Improving Diagnostic Effectiveness -- 7.9.1.Boosting -- 7.9.2.Ensemble Methods -- 7.9.3.Random Forests -- 7.10.Software for Decision Trees -- 7.11.Summary -- 8.Applying Decision Trees -- 8.1.Photographs -- 8.2.Ultrasound -- 8.3.MRI Images -- 8.4.EEGs and EMGs -- 8.5.Misclassification Costs -- 8.6.Receiver Operating Characteristic -- 8.7.When the Categories Are As Yet Undefined -- 8.7.1.Unsupervised Principal Components Applied to fMRI 8.7.2.Supervised Principal Components Applied to Microarrays -- 8.8.Ensemble Methods -- 8.9.Maximally Diversified Multiple Trees -- 8.10.Putting It All Together -- 8.11.Summary -- 8.12.To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- R1.Getting Started -- R1.1.R Functions -- R1.2.Vector Arithmetic -- R2.Store and Retrieve Data -- R2.1.Storing and Retrieving Files from Within R -- R2.2.The Tabular Format -- R2.3.Comma Separated Format -- R3.Resampling -- R3.1.The While Command -- R4.Expanding R's Capabilities -- R4.1.Downloading Libraries of R Functions -- R4.2.Programming Your Own Functions.
Beschreibung:	Includes bibliographical references and indexes
Beschreibung:	xii, 185 p ill 24 cm
ISBN:	0470927143 0-470-92714-3 9780470927144 978-0-470-92714-4