LEADER 06442nam 2200649 a 450 001 9910208830603321 005 20230725024416.0 010 $a1-283-13877-8 010 $a0-470-93725-4 010 $a9786613138774 010 $a0-470-93727-0 010 $a1-118-00214-8 035 $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(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$b[electronic resource] /$fPhillip I. Good 210 $aHoboken, N.J. $cWiley$dc2011 215 $axii, 185 p. $cill 311 $a0-470-92714-3 320 $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. 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 $a9910208830603321 996 $aAnalyzing the large numbers of variables in biomedical and satellite imagery$92118399 997 $aUNINA