7/12/2016. GROUP ANALYSIS Martin M. Monti UCLA Psychology AGGREGATING MULTIPLE SUBJECTS VARIANCE AT THE GROUP LEVEL
|
|
- Elijah Ford
- 5 years ago
- Views:
Transcription
1 GROUP ANALYSIS Martn M. Mont UCLA Psychology NITP AGGREGATING MULTIPLE SUBJECTS When we conduct mult-subject analyss we are tryng to understand whether an effect s sgnfcant across a group of people. Whether somethng s sgnfcant depends on the varance we assess t aganst: Classcal statstcal hypothess testng proceeds by comparng the dfference between the expected and hypotheszed effect aganst the yardstck of varance. [Holmes & Frston, 1998] VARIANCE AT THE GROUP LEVEL Fxed Effects (FFX): s about the ntra-subject varablty. An effect s compared aganst the yardstck of the precson wth whch t can be measured (for each subject). The dfferent subjects are consdered to be fxed. Random Effects (RFX): s about the nter-subject varablty. An effect s compared aganst the yardstck of how much varablty there s across dfferent subjects. The dfferent subjects are consdered to be a random sample from a greater populaton. Mxed Effects (MFX): s about ntra-subject & ntersubject varablty. 1
2 FIXED EFFECTS: INTRA-SUBJECT VARIABILITY Only varaton (over sessons) s measurement error True Response magntude s fxed Adapted from T Nchols RANDOM EFFECTS: INTER-SUBJECT VARIABILITY Source of varaton Response magntude Response magntude s random Each subject/sesson has random magntude But note, the populaton mean s fxed Adapted from T Nchols MIXED EFFECTS Two sources of varaton Measurement error Response magntude Response magntude s random Each subject/sesson has random magntude But note, the populaton mean s fxed Adapted from 7 T Nchols
3 IN OTHER WORDS FFX Model: y ~ (0, w ) d Subj. effect Meas. error IN OTHER WORDS FFX Model: y ~ (0, w ) d But d s a random varable! d d pop z z ~ (0, ) b Populaton effect Subj. varablty (around dpop) IN OTHER WORDS FFX Model: y ~ (0, w ) d But d s a random varable! d d z z ~ (0, ) pop MFX Model: y d pop Populaton effect z Subj. varablty (around d pop ) Meas. error b 3
4 IN OTHER WORDS FFX Model: y ~ (0, w ) d But d s a random varable! d d z z ~ (0, ) pop MFX Model: y d z ) pop b ( A HAIRY EXAMPLE Queston: Do M & F har dffer n length? erment: Take 5 hars from each of 8 Ss (4F, 4M) [ w=1, b=49] σ FFX : σ w Nn = = 0.01 σ RFX : σ b N = 49 4 = 1.5 σ MFX : σ w Nn + σ b N = 49 4 = 1.6 By Jeanette Mumford 4
5 GROUP ANALYSIS STRATEGIES: FFX FIXED V RANDOM Fxed sn t wrong, just usually sn t of nterest Fxed Effects Inference I can see an effect n ths sample Random Effects Inference I can extend my nference to the populaton: I expect to see the effect across the populaton 15 GROUP ANALYSIS STRATEGIES (I): ALL-IN-ONE Complete sngle-level GLM that relates varous parameters of nterest at the group level to the full set of (tme seres) data avalable Y XX g g Concatenated tmeseres Sngle subject desgn matrx Second level desgn matrx (e.g., pat v vol) Group level parameter error term 5
6 GROUP ANALYSIS STRATEGIES (I): ALL-IN-ONE Complete sngle-level GLM that relates varous parameters of nterest at the group level to the full set of (tme seres) data avalable Y XX g X g g Concatenated tmeseres Sngle subject desgn matrx Second level desgn matrx (e.g., pat v vol) Group level parameter GROUP ANALYSIS STRATEGIES (I): ALL-IN-ONE Complete sngle-level GLM that relates varous parameters of nterest at the group level to the full set of (tme seres) data avalable Computatonally ntense approach What f you acqure 1 more dataset? GROUP ANALYSIS STRATEGIES (II): THE SUMMARY STATISTIC APPROACH 6
7 GROUP ANALYSIS STRATEGIES (II): THE SUMMARY STATISTIC APPROACH GROUP ANALYSIS STRATEGIES (II): ND LEVEL 1. Perform an OLS [the SPM way] Assume that 1 st level varances ( w ) are the same for all subjects (.e., homoschedastcty)* Assume that 1 st level desgn matrces are the same for all subjects (.e., are balanced)* Estmate b from the (c) β carred forward from the 1 st level analyses, use t to assess the average group effect. Essentally, ths s a t-test! + Rapd & smple Are w truly the same (dstracted subjects, learnng, )? Are 1 st level matrces truly the same (forgotten v recalled)? GROUP ANALYSIS STRATEGIES (II): ND LEVEL. Perform a GLS (WLS) [the FSL way] Carry forward (c) β as well as 1 st level varance ( w ) Estmate b, defne (for each subject j) the overall varance s: σ wj + σ b Perform a GLS where each subject s ( nd level) data s weghted by her overall varance. + Bad subjects wth a large w wll be down-weghted + Statstcally more correct (presumably better for more usng desgns beyond smple t-test) Computatonally more ntensve (teratve calculaton of varance) 7
8 GROUP ANALYSIS STRATEGIES (II): THE SUMMARY STATISTIC APPROACH The debate: Frston (SPM): Assume homoscedastc 1 st level varances and do an OLS. Beckmann 03 (FSL): must use lower level varance n group estmaton, else no longer equvalent to the all-n-one approach Frston 05 (SPM): OLS s robust to unequal varances (but can estmate the covarance structure [usng ReML] from frst level [only sgnfcant voxels] and carry that forward). Smth 05 (FSL): Wthn subject varablty can actually be farly large Mumford 09: OLS s robust even n the presence of outlers and volatons of homoschedastcty, but only for 1 sample t-test. GLS always more optmal strategy. RECAP. FFX nferences are vald, but only wth respect to the sample. May be of nterest for sngle case studes, or small rare populatons you can fully sample.. MFX nferences are vald over the populaton you sample from because you are accountng for samplng varablty. Ths s what you want to do for a typcal group study.. The Summary statstc approach s effcent. Run 1 st levels ndependently, then combne the results. If you run 1 more subject, then you only have to re-run the group. MASSIVE UNIVARIATE APPROACH 8
9 Source: Jonathan Peelle HOW THESE DATA WERE GENERATED Source: Jonathan Peelle MULTIPLE COMPARISONS PROBLEM When you make 1 test, what s the probablty that a postve result s, n fact, not true (.e., false postve) a (say, 5%) If we make tests, what s the overall probablty (.e., famly-wse probablty) of false postves? 1 (1 a) (at a nomnal 5%: 9.75%) If we make n tests, what s the overall probablty (.e., famly-wse probablty) of false postves? 1 (1 a) n 9
10 MULTIPLE COMPARISONS PROBLEM How many tests do we perform n fmri analyss? Over (say) 100,000 null voxels, how many tmes wll we ncorrectly reject H 0? ~5,000 voxels (on average!) P r o b F P # Comparsons FISHY STATISTICS Stmul: pctures of faces (w/emotonal expressons). Task: determne what emotons depcted faces were experencng. Desgn: blocks of 1 sec actvaton/rest Analyss: standard data processng wth SPM Subject: 1 dead Atlantc Salmon. FALSE ACTIVATIONS UNDER H0 P < 0.05 (168 voxels) P < 0.01 (364 voxels) P < (3 voxels) 10
11 HOW MUCH CORRECTION? A B C t =.10, p < 0.05 (uncorrected) t = 3.60, p < (uncorrected) t = 7.15, p < 0.05, Bonferron Corrected Poor Specfcty (rsk of false postves) Good Power Good Specfcty Poor Power (rsk of false negatves) CORRECTION FOR MULTIPLE COMPARISONS man strateges: 1. Famly Wse Error (FWE): Control for the probablty of any false postves (e.g., Bonferron, Random Feld Theory, Permutaton). False Dscovery Rate (FDR): Control proporton of false postves among rejected tests FWE (I): BONFERRONI Man dea: make each ndvdual test more strngent, so overall you end up wth your total (.e., famly-wse) desred false postve rate. Bonf a FW n a P T a H a FW n ( 0) 1 For example: Desred famlywse false postve rate: a FW = 0.05 Total number of (ndependent) tests: 100,000 Then the Bonferron-corrected false postve level for each ndvdual test s: a Bonf a n FW ,000 11
12 FWE (I): BONFERRONI Assumes ndependent tests FMRI data spatally correlated (vasculature, spatal smoothng), so the number of ndependent tests s less than the number of voxels Overly strngent Increases Type II error Dffcult to fnd what s n n order to calculate the correct a bonf FWE (II): RANDOM FIELD THEORY Allows to fnd a threshold n a set of data where t s not easy (or even mpossble) to fnd the number of ndependent varables 3 step approach:. Estmate how smooth the data s ( resels ).Compute how many peaks would be above the threshold by chance ( Euler Characterstc ).Calculate the threshold that yelds desred FWER 1. SMOOTHNESS PARAMETRIZATION We can't compute the # of ndependent voxels, but we can compute the number of resoluton elements (.e. resels ). 1
13 . EULER CHARACTERISTIC Topologcal measure [c] Threshold an mage at u EC = # of blobs - # holes At hgh u: EC = # of blobs P(blob) = E[EC] Under H0, a FWE = E[EC] 3. THRESHOLD a FW E[ c] R(4log )( ) e 3 Z 1 Z t te Gven the smoothness of my data (R), what threshold (Z) do I need to set so that I have a FW chance (~E[EC]) of havng peak above threshold? FALSE DISCOVERY RATE (FDR) FDR controls the expected proporton of false postve values among supra-threshold values (.e., false clams v false tests): p < 0.05 FWE means: There s only a 5% chance any result s a false postve. p < 0.05 FDR means: No more than 5% of actve voxels are false postves. 13
14 FALSE DISCOVERY RATE (FDR) COMPARING CORRECTION METHODS Sgnal Nose Sgnal+Nose NO CORRECTION (a = 0.1) On average, 10% of the 'false' voxels are ncorrectly declared actve. In each experment we have about 10% false alarms 14
15 FWE (a = 0.1) FDR (a = 0.1) RESOURCES Mont M.M. (011) Statstcal analyss of fmri tme-seres: A crtcal evaluaton of the GLM approach. Fronters n Human Neuroscence, 5(8). Mumford, J. A., and Nchols, T. (009). Smple group fmri modelng and nference. Neuromage 47, Mumford, J. A., and Poldrack, R. A. (007). Modelng group fmri data. Soc. Cogn. Affect. Neurosc., Beckmann, C. F., Jenknson, M., and Smth, S. M. (003). General multlevel lnear modelng for group analyss n fmri. Neuromage 0, Poldrack R.A., Mumford J.A., Nchols T.E. (011) Handbook of Functonal MRI Analyss, Cambrdge Unversty Press. Lazar, N. (008). The statstcal analyss of functonal MRI data. Sprnger. Frston K.J., et al Statstcal Parametrc Mappng: The Analyss of Functonal Bran Images, chapter 8. 15
C2 Training: June 8 9, Combining effect sizes across studies. Create a set of independent effect sizes. Introduction to meta-analysis
C2 Tranng: June 8 9, 2010 Introducton to meta-analyss The Campbell Collaboraton www.campbellcollaboraton.org Combnng effect szes across studes Compute effect szes wthn each study Create a set of ndependent
More informationExtending the GLM. Outline. Mixed effects motivation Evaluating mixed effects methods Three methods. Conclusions. Overview
Extending the GLM So far, we have considered the GLM for one run in one subject The same logic can be applied to multiple runs and multiple subjects GLM Stats For any given region, we can evaluate the
More informationValid conjunction inference with the minimum statistic
www.elsever.com/locate/ynmg NeuroImage 25 (2005) 653 660 Vald conjuncton nference wth the mnmum statstc Thomas Nchols, a, * Matthew Brett, b Jesper Andersson, c Tor Wager, d and Jean-Baptste Polne e a
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationA Statistical Model Selection Strategy Applied to Neural Networks
A Statstcal Model Selecton Strategy Appled to Neural Networks Joaquín Pzarro Elsa Guerrero Pedro L. Galndo joaqun.pzarro@uca.es elsa.guerrero@uca.es pedro.galndo@uca.es Dpto Lenguajes y Sstemas Informátcos
More informationCorrection for multiple comparisons. Cyril Pernet, PhD SBIRC/SINAPSE University of Edinburgh
Correction for multiple comparisons Cyril Pernet, PhD SBIRC/SINAPSE University of Edinburgh Overview Multiple comparisons correction procedures Levels of inferences (set, cluster, voxel) Circularity issues
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationSimulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010
Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationNeuroImage 60 (2012) Contents lists available at SciVerse ScienceDirect. NeuroImage. journal homepage:
NeuroImage 60 (0) 747 765 Contents lsts avalable at ScVerse ScenceDrect NeuroImage journal homepage: www.elsever.com/locate/ynmg FMRI group analyss combnng effect estmates and ther varances Gang Chen a,,
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationy and the total sum of
Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton
More informationNew and best-practice approaches to thresholding
New and best-practice approaches to thresholding Thomas Nichols, Ph.D. Department of Statistics & Warwick Manufacturing Group University of Warwick FIL SPM Course 17 May, 2012 1 Overview Why threshold?
More informationControlling for multiple comparisons in imaging analysis. Wednesday, Lecture 2 Jeanette Mumford University of Wisconsin - Madison
Controlling for multiple comparisons in imaging analysis Wednesday, Lecture 2 Jeanette Mumford University of Wisconsin - Madison Motivation Run 100 hypothesis tests on null data using p
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More informationUnsupervised Learning and Clustering
Unsupervsed Learnng and Clusterng Why consder unlabeled samples?. Collectng and labelng large set of samples s costly Gettng recorded speech s free, labelng s tme consumng 2. Classfer could be desgned
More informationNAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics
Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson
More informationData Mining: Model Evaluation
Data Mnng: Model Evaluaton Aprl 16, 2013 1 Issues: Evaluatng Classfcaton Methods Accurac classfer accurac: predctng class label predctor accurac: guessng value of predcted attrbutes Speed tme to construct
More informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationRecognizing Faces. Outline
Recognzng Faces Drk Colbry Outlne Introducton and Motvaton Defnng a feature vector Prncpal Component Analyss Lnear Dscrmnate Analyss !"" #$""% http://www.nfotech.oulu.f/annual/2004 + &'()*) '+)* 2 ! &
More information7/15/2016 ARE YOUR ANALYSES TOO WHY IS YOUR ANALYSIS PARAMETRIC? PARAMETRIC? That s not Normal!
ARE YOUR ANALYSES TOO PARAMETRIC? That s not Normal! Martin M Monti http://montilab.psych.ucla.edu WHY IS YOUR ANALYSIS PARAMETRIC? i. Optimal power (defined as the probability to detect a real difference)
More informationEdge Detection in Noisy Images Using the Support Vector Machines
Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona
More informationLecture #15 Lecture Notes
Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal
More informationMultiple comparisons problem and solutions
Multiple comparisons problem and solutions James M. Kilner http://sites.google.com/site/kilnerlab/home What is the multiple comparisons problem How can it be avoided Ways to correct for the multiple comparisons
More informationModeling Local Uncertainty accounting for Uncertainty in the Data
Modelng Local Uncertanty accontng for Uncertanty n the Data Olena Babak and Clayton V Detsch Consder the problem of estmaton at an nsampled locaton sng srrondng samples The standard approach to ths problem
More informationICA Denoising for Event-Related fmri Studies
Proceedngs of the 005 IEEE Engneerng n Medcne and Bology 7th Annual Conference Shangha, Chna, September -4, 005 ICA Denosng for Event-Related fmri Studes Martn J. McKeown -3, Yong-e Hu, and Z. Jane Wang
More informationPerformance Evaluation of Information Retrieval Systems
Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence
More informationStatistical Data Set Comparison for Continuous, Dependent Data by T. C. Smith
Sesson 3 Operatonal and Specal Issues Statstcal Data Set Comparson for Contnuous, Dependent Data by T. C. Smth Tmothy C. Smth Davd Taylor Model Basn (NSWC/CD) ABSTRACT Classcal statstcal methods exst for
More informationFitting & Matching. Lecture 4 Prof. Bregler. Slides from: S. Lazebnik, S. Seitz, M. Pollefeys, A. Effros.
Fttng & Matchng Lecture 4 Prof. Bregler Sldes from: S. Lazebnk, S. Setz, M. Pollefeys, A. Effros. How do we buld panorama? We need to match (algn) mages Matchng wth Features Detect feature ponts n both
More informationOutline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1
4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationFEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur
FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents
More informationAn Entropy-Based Approach to Integrated Information Needs Assessment
Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology
More informationSupport Vector Machines
Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned
More informationMathematics 256 a course in differential equations for engineering students
Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the
More informationLecture 4: Principal components
/3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness
More informationOptimizing Document Scoring for Query Retrieval
Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng
More informationMachine Learning: Algorithms and Applications
14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of
More informationSteps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices
Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between
More informationEYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS
P.G. Demdov Yaroslavl State Unversty Anatoly Ntn, Vladmr Khryashchev, Olga Stepanova, Igor Kostern EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS Yaroslavl, 2015 Eye
More informationClassification of brain activation via spatial Bayesian variable selection in fmri regression
Adv Data Anal Classf (2014) 8:63 83 DOI 10.1007/s11634-013-0142-6 REGULAR ARTICLE Classfcaton of bran actvaton va spatal Bayesan varable selecton n fmri regresson Stefane Kalus Phlpp G. Sämann Ludwg Fahrmer
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationMultilevel Analysis with Informative Weights
Secton on Survey Research Methods JSM 2008 Multlevel Analyss wth Informatve Weghts Frank Jenkns Westat, 650 Research Blvd., Rockvlle, MD 20850 Abstract Multlevel modelng has become common n large scale
More informationEmpirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap
Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*
More informationContent Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers
IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth
More informationCategories and Subject Descriptors B.7.2 [Integrated Circuits]: Design Aids Verification. General Terms Algorithms
3. Fndng Determnstc Soluton from Underdetermned Equaton: Large-Scale Performance Modelng by Least Angle Regresson Xn L ECE Department, Carnege Mellon Unversty Forbs Avenue, Pttsburgh, PA 3 xnl@ece.cmu.edu
More informationMultiple Testing and Thresholding
Multiple Testing and Thresholding NITP, 2010 Thanks for the slides Tom Nichols! Overview Multiple Testing Problem Which of my 100,000 voxels are active? Two methods for controlling false positives Familywise
More informationWavefront Reconstructor
A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen 14-12-201212 2012 Delft Unversty of Technology Contents Introducton Wavefront reconstructon usng Smplex B-Splnes
More informationLECTURE : MANIFOLD LEARNING
LECTURE : MANIFOLD LEARNING Rta Osadchy Some sldes are due to L.Saul, V. C. Raykar, N. Verma Topcs PCA MDS IsoMap LLE EgenMaps Done! Dmensonalty Reducton Data representaton Inputs are real-valued vectors
More informationDesign of Georeference-Based Emission Activity Modeling System (G-BEAMS) for Japanese Emission Inventory Management
1 13 th Internatonal Emsson Inventory Conference June 7-10, 2004 Clearwater, Florda Sesson 7 Data Management Desgn of Georeference-Based Emsson Actvty Modelng System (G-BEAMS) for Japanese Emsson Inventory
More informationAn Ensemble Learning algorithm for Blind Signal Separation Problem
An Ensemble Learnng algorthm for Blnd Sgnal Separaton Problem Yan L 1 and Peng Wen 1 Department of Mathematcs and Computng, Faculty of Engneerng and Surveyng The Unversty of Southern Queensland, Queensland,
More informationAdjustment methods for differential measurement errors in multimode surveys
Adjustment methods for dfferental measurement errors n multmode surveys Salah Merad UK Offce for Natonal Statstcs ESSnet MM DCSS, Fnal Meetng Wesbaden, Germany, 4-5 September 2014 Outlne Introducton Stablsng
More informationOutline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:
Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A
More informationA Novel Adaptive Descriptor Algorithm for Ternary Pattern Textures
A Novel Adaptve Descrptor Algorthm for Ternary Pattern Textures Fahuan Hu 1,2, Guopng Lu 1 *, Zengwen Dong 1 1.School of Mechancal & Electrcal Engneerng, Nanchang Unversty, Nanchang, 330031, Chna; 2. School
More informationAPPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT
3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationModeling Waveform Shapes with Random Effects Segmental Hidden Markov Models
Modelng Waveform Shapes wth Random Effects Segmental Hdden Markov Models Seyoung Km, Padhrac Smyth Department of Computer Scence Unversty of Calforna, Irvne CA 9697-345 {sykm,smyth}@cs.uc.edu Abstract
More informationA CLASS OF TRANSFORMED EFFICIENT RATIO ESTIMATORS OF FINITE POPULATION MEAN. Department of Statistics, Islamia College, Peshawar, Pakistan 2
Pa. J. Statst. 5 Vol. 3(4), 353-36 A CLASS OF TRANSFORMED EFFICIENT RATIO ESTIMATORS OF FINITE POPULATION MEAN Sajjad Ahmad Khan, Hameed Al, Sadaf Manzoor and Alamgr Department of Statstcs, Islama College,
More informationGroup Analysis of Functional Imaging Data using. Penalized Maximum Likelihood
Group Analyss of Functonal Imagng Data usng Penalzed Maxmum Lelhood Rao P. Gullapall Ranjan Matra 2 Steven R. Roys Joel Greenspan 3 Gerald V. Smth 4 Gad Alon 4 Department of Radology Unversty of Maryland
More informationMulticlass Object Recognition based on Texture Linear Genetic Programming
Multclass Object Recognton based on Texture Lnear Genetc Programmng Gustavo Olague 1, Eva Romero 1 Leonardo Trujllo 1, and Br Bhanu 2 1 CICESE, Km. 107 carretera Tjuana-Ensenada, Mexco, olague@ccese.mx,
More informationSynthesizer 1.0. User s Guide. A Varying Coefficient Meta. nalytic Tool. Z. Krizan Employing Microsoft Excel 2007
Syntheszer 1.0 A Varyng Coeffcent Meta Meta-Analytc nalytc Tool Employng Mcrosoft Excel 007.38.17.5 User s Gude Z. Krzan 009 Table of Contents 1. Introducton and Acknowledgments 3. Operatonal Functons
More informationSolving two-person zero-sum game by Matlab
Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by
More informationA Robust Parametric Method for Bias Field Estimation and Segmentation of MR Images
A Robust Parametrc Method for Bas Feld Estmaton and Segmentaton of MR Images Chunmng L, Chrs Gatenby,LWang 2, John C. Gore Vanderblt Unversty of Imagng Scence, Nashvlle, TN 37232, USA 2 Nanjng Unversty
More informationIntroductory Concepts for Voxel-Based Statistical Analysis
Introductory Concepts for Voxel-Based Statistical Analysis John Kornak University of California, San Francisco Department of Radiology and Biomedical Imaging Department of Epidemiology and Biostatistics
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationOutlier Detection based on Robust Parameter Estimates
Outler Detecton based on Robust Parameter Estmates Nor Azlda Aleng 1, Ny Ny Nang, Norzan Mohamed 3 and Kasyp Mokhtar 4 1,3 School of Informatcs and Appled Mathematcs, Unverst Malaysa Terengganu, 1030 Kuala
More informationActive Contours/Snakes
Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng
More informationA Non-Parametric Mixture Model for the fmri Visual Field Map
A Non-Parametrc Mxture Model for the fmri Vsual Feld Map Raymond G. Hoffmann 1, Ncholas Pajewsk 2, Edgar A Deyoe 3, Danel B Rowe 4 1 Quanttatave Health Scences, Pedatrcs, Medcal College of Wsconsn, Mlwaukee,
More informationFuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches
Proceedngs of the Internatonal Conference on Cognton and Recognton Fuzzy Flterng Algorthms for Image Processng: Performance Evaluaton of Varous Approaches Rajoo Pandey and Umesh Ghanekar Department of
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationEconometrics 2. Panel Data Methods. Advanced Panel Data Methods I
Panel Data Methods Econometrcs 2 Advanced Panel Data Methods I Last tme: Panel data concepts and the two-perod case (13.3-4) Unobserved effects model: Tme-nvarant and dosyncratc effects Omted varables
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationHermite Splines in Lie Groups as Products of Geodesics
Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the
More informationPresentation Outline. Effective Survey Sampling of Rare Subgroups Probability-Based Sampling Using Split-Frames with Listed Households
Effectve Survey Samplng of Rare Subgroups Probablty-Based Samplng Usng Splt-Frames wth Lsted Households Nature of the Problem Presentaton Outlne Samplng Alternatves Dsproportonal Stratfed Samplng Mansour
More informationFace Recognition University at Buffalo CSE666 Lecture Slides Resources:
Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural
More informationTECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS. Muradaliyev A.Z.
TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS Muradalyev AZ Azerbajan Scentfc-Research and Desgn-Prospectng Insttute of Energetc AZ1012, Ave HZardab-94 E-mal:aydn_murad@yahoocom Importance of
More informationReview of approximation techniques
CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated
More informationWhy visualisation? IRDS: Visualization. Univariate data. Visualisations that we won t be interested in. Graphics provide little additional information
Why vsualsaton? IRDS: Vsualzaton Charles Sutton Unversty of Ednburgh Goal : Have a data set that I want to understand. Ths s called exploratory data analyss. Today s lecture. Goal II: Want to dsplay data
More informationMultiple Testing and Thresholding
Multiple Testing and Thresholding NITP, 2009 Thanks for the slides Tom Nichols! Overview Multiple Testing Problem Which of my 100,000 voxels are active? Two methods for controlling false positives Familywise
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationTN348: Openlab Module - Colocalization
TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages
More informationIntegrated Expression-Invariant Face Recognition with Constrained Optical Flow
Integrated Expresson-Invarant Face Recognton wth Constraned Optcal Flow Chao-Kue Hseh, Shang-Hong La 2, and Yung-Chang Chen Department of Electrcal Engneerng, Natonal Tsng Hua Unversty, Tawan 2 Department
More informationAvailable online at ScienceDirect. Procedia Environmental Sciences 26 (2015 )
Avalable onlne at www.scencedrect.com ScenceDrect Proceda Envronmental Scences 26 (2015 ) 109 114 Spatal Statstcs 2015: Emergng Patterns Calbratng a Geographcally Weghted Regresson Model wth Parameter-Specfc
More informationMULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION WITH MULTIPLE FEATURES
MULISPECRAL REMOE SESIG IMAGE CLASSIFICAIO WIH MULIPLE FEAURES QIA YI, PIG GUO, Image Processng and Pattern Recognton Laboratory, Bejng ormal Unversty, Bejng 00875, Chna School of Computer Scence and echnology,
More informationAn Improved Image Segmentation Algorithm Based on the Otsu Method
3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu,
More informationMultiple Testing and Thresholding
Multiple Testing and Thresholding UCLA Advanced NeuroImaging Summer School, 2007 Thanks for the slides Tom Nichols! Overview Multiple Testing Problem Which of my 100,000 voxels are active? Two methods
More informationFusion Performance Model for Distributed Tracking and Classification
Fuson Performance Model for Dstrbuted rackng and Classfcaton K.C. Chang and Yng Song Dept. of SEOR, School of I&E George Mason Unversty FAIRFAX, VA kchang@gmu.edu Martn Lggns Verdan Systems Dvson, Inc.
More informationAn Image Fusion Approach Based on Segmentation Region
Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua
More informationREMOTE SENSING REQUIREMENTS DEVELOPMENT: A SIMULATION-BASED APPROACH
REMOTE SENSING REQUIREMENTS DEVEOPMENT: A SIMUATION-BASED APPROAC V. Zanon a, B. Davs a, R. Ryan b, G. Gasser c, S. Blonsk b a Earth Scence Applcatons Drectorate, Natonal Aeronautcs and Space Admnstraton,
More informationMulti-stable Perception. Necker Cube
Mult-stable Percepton Necker Cube Spnnng dancer lluson, Nobuuk Kaahara Fttng and Algnment Computer Vson Szelsk 6.1 James Has Acknowledgment: Man sldes from Derek Hoem, Lana Lazebnk, and Grauman&Lebe 2008
More informationA Workflow for Spatial Uncertainty Quantification using Distances and Kernels
A Workflow for Spatal Uncertanty Quantfcaton usng Dstances and Kernels Célne Schedt and Jef Caers Stanford Center for Reservor Forecastng Stanford Unversty Abstract Assessng uncertanty n reservor performance
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationU.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 5 Luca Trevisan September 7, 2017
U.C. Bereley CS294: Beyond Worst-Case Analyss Handout 5 Luca Trevsan September 7, 207 Scrbed by Haars Khan Last modfed 0/3/207 Lecture 5 In whch we study the SDP relaxaton of Max Cut n random graphs. Quc
More informationAnalysis of Malaysian Wind Direction Data Using ORIANA
Modern Appled Scence March, 29 Analyss of Malaysan Wnd Drecton Data Usng ORIANA St Fatmah Hassan (Correspondng author) Centre for Foundaton Studes n Scence Unversty of Malaya, 63 Kuala Lumpur, Malaysa
More informationUser Authentication Based On Behavioral Mouse Dynamics Biometrics
User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA
More information