LEADER 08472nam 2200769 a 450 001 9910825098303321 005 20240516065423.0 010 $a9786613138774 010 $a9781283138772 010 $a1283138778 010 $a9780470937259 010 $a0470937254 010 $a9780470937273 010 $a0470937270 010 $a9781118002148 010 $a1118002148 035 $a(CKB)4330000000000530 035 $a(MiAaPQ)EBC706906 035 $a(MiAaPQ)EBC4030238 035 $a(Au-PeEL)EBL706906 035 $a(CaPaEBR)ebr10452145 035 $a(CaONFJC)MIL313877 035 $a(OCoLC)708036686 035 $a(OCoLC)653476377 035 $a(FINmELB)ELB179585 035 $a(Perlego)1011100 035 $a(EXLCZ)994330000000000530 100 $a20100805d2011 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aAnalyzing the large numbers of variables in biomedical and satellite imagery /$fPhillip I. Good 205 $a1st ed. 210 $aHoboken, N.J. $cWiley$dc2011 215 $axii, 185 p. $cill 311 08$a9780470927144 311 08$a0470927143 320 $aIncludes bibliographical references and indexes. 327 $gMachine generated contents note:$g1.$tVery Large Arrays --$g1.1.$tApplications --$g1.2.$tProblems --$g1.3.$tSolutions --$g2.$tPermutation Tests --$g2.1.$tTwo-Sample Comparison --$g2.1.1.$tBlocks --$g2.2.$tk-Sample Comparison --$g2.3.$tComputing The p-Value --$g2.3.1.$tMonte Carlo Method --$g2.3.2.$tAn R Program --$g2.4.$tMultiple-Variable Comparisons --$g2.4.1.$tEuclidean Distance Matrix Analysis --$g2.4.2.$tHotelling's T2 --$g2.4.3.$tMantel's U --$g2.4.4.$tCombining Univariate Tests --$g2.4.5.$tGene Set Enrichment Analysis --$g2.5.$tCategorical Data --$g2.6.$tSoftware --$g2.7.$tSummary --$g3.$tApplying the Permutation Test --$g3.1.$tWhich Variables Should Be Included? --$g3.2.$tSingle-Value Test Statistics --$g3.2.1.$tCategorical Data --$g3.2.2.$tA Multivariate Comparison Based on a Summary Statistic --$g3.2.3.$tA Multivariate Comparison Based on Variants of Hotelling's T2 327 $g3.2.4.$tAdjusting for Covariates --$g3.2.5.$tPre-Post Comparisons --$g3.2.6.$tChoosing a Statistic: Time-Course Microarrays --$g3.3.$tRecommended Approaches --$g3.4.$tTo Learn More --$g4.$tBiological Background --$g4.1.$tMedical Imaging --$g4.1.1.$tUltrasound --$g4.1.2.$tEEG/MEG --$g4.1.3.$tMagnetic Resonance Imaging --$g4.1.3.1.$tMRI --$g4.1.3.2.$tfMRI --$g4.1.4.$tPositron Emission Tomography --$g4.2.$tMicroarrays --$g4.3.$tTo Learn More --$g5.$tMultiple Tests --$g5.1.$tReducing the Number of Hypotheses to Be Tested --$g5.1.1.$tNormalization --$g5.1.2.$tSelection Methods --$g5.1.2.1.$tUnivariate Statistics --$g5.1.2.2.$tWhich Statistic? --$g5.1.2.3.$tHeuristic Methods --$g5.1.2.4.$tWhich Method? --$g5.2.$tControlling the Over All Error Rate --$g5.2.1.$tAn Example: Analyzing Data from Microarrays --$g5.3.$tControlling the False Discovery Rate --$g5.3.1.$tAn Example: Analyzing Time-Course Data from Microarrays --$g5.4.$tGene Set Enrichment Analysis 327 $g5.5.$tSoftware for Performing Multiple Simultaneous Tests --$g5.5.1.$tAFNI --$g5.5.2.$tCyber-T --$g5.5.3.$tdChip --$g5.5.4.$tExactFDR --$g5.5.5.$tGESS --$g5.5.6.$tHaploView --$g5.5.7.$tMatLab --$g5.5.8.$tR --$g5.5.9.$tSAM --$g5.5.10.$tParaSam --$g5.6.$tSummary --$g5.7.$tTo Learn More --$g6.$tThe Bootstrap --$g6.1.$tSamples and Populations --$g6.2.$tPrecision of an Estimate --$g6.2.1.$tR Code --$g6.2.2.$tApplying the Bootstrap --$g6.2.3.$tBootstrap Reproducibility Index --$g6.2.4.$tEstimation in Regression Models --$g6.3.$tConfidence Intervals --$g6.3.1.$tTesting for Equivalence --$g6.3.2.$tParametric Bootstrap --$g6.3.3.$tBlocked Bootstrap --$g6.3.4.$tBalanced Bootstrap --$g6.3.5.$tAdjusted Bootstrap --$g6.3.6.$tWhich Test? --$g6.4.$tDetermining Sample Size --$g6.4.1.$tEstablish a Threshold --$g6.5.$tValidation --$g6.5.1.$tCluster Analysis --$g6.5.2.$tCorrespondence Analysis --$g6.6.$tBuilding a Model --$g6.7.$tHow Large Should The Samples Be? 327 $g6.8.$tSummary --$g6.9.$tTo Learn More --$g7.$tClassification Methods --$g7.1.$tNearest Neighbor Methods --$g7.2.$tDiscriminant Analysis --$g7.3.$tLogistic Regression --$g7.4.$tPrincipal Components --$g7.5.$tNaive Bayes Classifier --$g7.6.$tHeuristic Methods --$g7.7.$tDecision Trees --$g7.7.1.$tA Worked-Through Example --$g7.8.$tWhich Algorithm Is Best for Your Application? --$g7.8.1.$tSome Further Comparisons --$g7.8.2.$tValidation Versus Cross-validation --$g7.9.$tImproving Diagnostic Effectiveness --$g7.9.1.$tBoosting --$g7.9.2.$tEnsemble Methods --$g7.9.3.$tRandom Forests --$g7.10.$tSoftware for Decision Trees --$g7.11.$tSummary --$g8.$tApplying Decision Trees --$g8.1.$tPhotographs --$g8.2.$tUltrasound --$g8.3.$tMRI Images --$g8.4.$tEEGs and EMGs --$g8.5.$tMisclassification Costs --$g8.6.$tReceiver Operating Characteristic --$g8.7.$tWhen the Categories Are As Yet Undefined --$g8.7.1.$tUnsupervised Principal Components Applied to fMRI 327 $g8.7.2.$tSupervised Principal Components Applied to Microarrays --$g8.8.$tEnsemble Methods --$g8.9.$tMaximally Diversified Multiple Trees --$g8.10.$tPutting It All Together --$g8.11.$tSummary --$g8.12.$tTo Learn More --$tGlossary of Biomedical Terminology --$tGlossary of Statistical Terminology --$tAppendix: An R Primer --$gR1.$tGetting Started --$gR1.1.$tR Functions --$gR1.2.$tVector Arithmetic --$gR2.$tStore and Retrieve Data --$gR2.1.$tStoring and Retrieving Files from Within R --$gR2.2.$tThe Tabular Format --$gR2.3.$tComma Separated Format --$gR3.$tResampling --$gR3.1.$tThe While Command --$gR4.$tExpanding R's Capabilities --$gR4.1.$tDownloading Libraries of R Functions --$gR4.2.$tProgramming Your Own Functions. 330 8 $aThis book grew out of an online interactive offered through statcourse.com, and it soon became apparent to the author that the course was too limited in terms of time and length in light of the broad backgrounds of the enrolled students. The statisticians who took the course needed to be brought up to speed both on the biological context as well as on the specialized statistical methods needed to handle large arrays. Biologists and physicians, even though fully knowledgeable concerning the procedures used to generate microaarrays, EEGs, or MRIs, needed a full introduction to the resampling methods-the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays could be introduced. As the intended audience for this book consists both of statisticians and of medical and biological research workers as well as all those research workers who make use of satellite imagery including agronomists and meteorologists, the book provides a step-by-step approach to not only the specialized methods needed to analyze the data from microarrays and images, but also to the resampling methods, step-down multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis have been introduced in the past decade, the author has selected only those techniques for which software is available along with a list of the available links from which the software may be purchased or downloaded without charge. Topical coverage includes: very large arrays; permutation tests; applying permutation tests; gathering and preparing data for analysis; multiple tests; bootstrap; applying the bootstrap; classification methods; decision trees; and applying decision trees. 606 $aData mining 606 $aMathematical statistics 606 $aBiomedical engineering$xData processing 606 $aRemote sensing$xData processing 606 $aFunctions of several complex variables 606 $aR (Computer program language) 615 0$aData mining. 615 0$aMathematical statistics. 615 0$aBiomedical engineering$xData processing. 615 0$aRemote sensing$xData processing. 615 0$aFunctions of several complex variables. 615 0$aR (Computer program language) 676 $a006.3/12 700 $aGood$b Phillip I$0102489 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910825098303321 996 $aAnalyzing the large numbers of variables in biomedical and satellite imagery$94091122 997 $aUNINA