Home Page for UNC - STOR 891

Object Oriented Data Analysis

Fall Semester, 2012

Lecture notes:

  1. Tuesday, Aug. 21 - Power Point Notes - Organizational Matters - What is OODA? - Visualization by Projection - Object Space and Feature Space - Curves as Data Objects - Data Representation Issues - PCA Visualization - Mortality Data

  2. Thursday, Aug. 23 - Power Point Notes - PCA Terminology - Time Series of Curves & Color Coding - Chemo-metric Data - Glioblastoma Data & Brushing - Limitations of PCA - NCI 60 Data - Directions Beyond PCA, DWD

  3. Tuesday, Aug. 28 - Power Point Notes - Gene Cell Cycle Data - Microarrays, Data Visualization, and Batch Adjustment - Matlab Software [Matlab Script File used as example] - Cornea Data

  4. Thursday, Aug. 30 - Power Point Notes - Cornea Data - Robust HDLSS (Spherical) PCA - Elliptical PCA -

  5. Tuesday, Sep. 4 - Power Point Notes - Marginal Distribution Checking - Data Transformation - Clusters & PCA - Mass Flux Data - Smoothing Basics - Bandwidth Selection - SiZer

  6. Thursday, Sep. 6 - Power Point Notes - Finish SiZer - Revisit Mass Flux Data - SiZer Analysis of Cell Cycle Data - Classification - Fisher Linear Discrimination -

  7. Tuesday, Sep. 11 - Power Point Notes - HDLSS Discrimination - Kernel Methods

  8. Thursday, Sep. 13 - Power Point Notes - Support Vector Machines- Distance Weighted Discrimination

  9. Tuesday, Sep. 18 - Power Point Notes - DWD & Survival Data - DWD Simulations - DWD & SVM Tuning - Melanoma Data & ROC Curve - Clustering

  10. Thursday, Sep. 20 - Power Point Notes - SWISS score - SigClust, QQ Envelope plot  PP: Juan Carlos Prietos - Texture Synthesis

  11. Tuesday, Sep. 25 - Power Point Notes - Finish QQ Envelope Plot & SigClust - Linear Algebra Review

  12. Thursday, Sep. 27 - Power Point Notes - Multivariate Probability Review - PCA Folklore - PCA as an optimization Problem - PCA Mathematics and Graphics - PCA Redistribution of Energy - PCA Data Representation & Simulation

  13. Tuesday, Oct. 2 - Power Point Notes - Alternate PCA Computation & SVD - Primal & Dual PCA - SVD Data Analysis & Recentering

  14. Thursday, Oct. 4 - Power Point Notes - Primal & Dual PCA - SVD Data Analysis & Recentering - HDLSS asymptotics - PP: Heather Couture - Tissue Classification

  15. Tuesday, Oct. 9 - PDF Notes - Guest Lecture - Susan Wei - DiProPerm High Dimensional Hypothesis Testing

  16. Thursday, Oct. 11 - PDF Notes - Guest Lecture, Dan Shen - HDLSS Sparse PCA & Big Picture PCA Asymptotics

  17. Tuesday, Oct. 16 - PDF Notes - Guest Lecture - Xiaosun Lu - Cell-Well Data Objects & Fisher Rao Curve Warping

  18. Thursday, Oct. 18 - No Class - University Fall Break

  19. Tuesday, Oct. 23 - Power Point Notes - HDLSS Asymptotics - Kernel Methods in High Dimensions - Introduction to Shape Statistics - Directional Data - PP: Patrick Kimes - Introduction to L-Statistics

  20. Thursday, Oct. 25 - Power Point Notes - Image Analysis & OODA - Shape Representations - Bladder Prostate Rectum Data - Data on Manifolds - Principal Geodesic Analysis - PP: Chong Shao - Multi-Object Statistics   

  21. Tuesday, Oct. 30 - Power Point Notes - Principal Geodesic Analysis - Principal Nested Spheres -  Backwards PCA - PP: Jared Vicory - Statistics on S-rep Differences - PP: Yazong Gao - Fast Prostate Localization

  22. Thursday, Nov. 1 - Power Point Notes - Composite Principal Nested Spheres - Variation on Shape Analysis: Transformations as Data Objects - Trees as Data -  PP: Yen Low - Intro to QSAR
  23. Tuesday, Nov. 6 - Power Point Notes - Trees as Data, Combinatorial Approach, D-L Visualization - PP: Beatriz Paniagua - Quantification of 3D Bony Changes in Temporomandibular Joint Osteoarthritis - PP: Jie Xiong - PCA/DWD on Next Gen Sequencing Data - PP: Lin Wu - Data Visualization

  24. Thursday, Nov. 8 - Power Point Notes -Trees as Data, Combinatorial PCA, D-L view, Begin Phylogenetic Trees   - PP: Gen Li - Biclustering Classification - PP: James Wilson - Clustering on Networks - PP: Guan Yu - Outlier Detection in Functional Observations - PP: Tian Cao - Multimodal Registration    

  25. Tuesday, Nov. 13 - PDF Notes - Guest Lecture - Lingsong Zhang - Nonnegative Matrix Factorization

  26. Thursday, Nov. 15 - PDF Notes - Guest Lecture - Eric Lock - Joint and Individual Variation Explained

  27. Tuesday, Nov. 20 - Power Point Notes - Phylogenetic Trees, Common Leafs for Artery Trees, Edges as Splits - PP: Di Miao - JIVE on Glioblastoma Data  - PP: Yi Hong - Statistics on Pediatric Airway - PP: Jenny Shi - Some Statistical Approaches to RNA Substitution Analysis   

  28. Thursday, Nov. 22 - No Class - University Holiday: Thankgiving

  29. Tuesday, Nov. 27 - Power Point Notes - Phylogenetic Trees, Geodesics, Frechet Mean - PP: Qianwen Liu - Weather Data Analysis - PP: Ben Morris - Functional analysis of ecological communities - PP: Yuying Xie - Joint Estimation of Multiple Dependent Gaussian Graphical Models - PP: Zane Blanton - Boosted Regression Models 

  30. Thursday, Nov. 29 - Power Point Notes - Phylogenetic Trees, Multidimensional Scaling, Negative Curvature of Space - PP: Qing Feng  - Model selection methods for classification of Melanoma data - PP: Haojin Zhai - A polynomial time algorithm for computing geodesic distance in tree space - PP: Yang Liu - Factor analysis of binary item response data - PP: Eunjee Lee - Visualization of bottleneck distances in a persistence diagram 

  31. Tuesday, Dec. 4 - Power Point Notes - Independent Component Analysis - PP: Dongqing Yu - Nondurable Goods Index (Ramsay and Silverman Casebook) - PP: Simi Wang - Clustering in Network Data - PP: Bryan Jung - SPHARM and its applications - PP: Joseph Lavalle-Rivera - Human Growth Functional Data


Potential Future Topics:


Ahn, J., Marron, J. S., Muller, K. M. and Chi, Y. – Y. (2007) The High Dimension, Low Sample Size Geometric Representation Holds Under Mild Conditions, Biometrika, 94, 760-766  (cited 10/23/12)

Aoshima, M. (2010) Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix, Journal of Multivariate Analysis, 101, 2060-2077 (cited 10/23/12)

Aydin, B., Pataki, G., Wang, H., Bullitt, E. and Marron, J. S. (2009) A principal component analysis for trees, Annals of Applied Statistics, 3, 1597-1615 (cited 10/23/12)

Aydin, B., Pataki, G., Wang, H., Ladha, A., Bullitt, E. Marron, J. S. (2011) Visualizing the Structure of Large Trees, Electronic Journal of Statistics, 5, 405-420

Benito, M. Parker, J. Du, Q., Wu, X. Xiang, D., Perou, C. M. and Marron, J. S., (2004) Adjustment of systematic microarray data biases, Bioinformatics, 20, 105-114, PMID: 14693816 (cited 8/23/12)

Bickel, P. J. and Levina, E. (2004) Some theory for Fisher's Linear Discriminant function, "naive Bayes", and some alternatives when there are many more variables than observations, Bernoulli, 10, 989-1010 (cited 9/6/12)

Billera L, Holmes S, & Vogtmann K (2001) Geometry of the Space of Phylogenetic Trees, Advances in Applied Mathematics 27, 733–767 (cited 11/8/12)

Bookstein, F. L. (1991). Morphometric Tools for Landmark Data, Cambridge: Cambridge University Press (cited 10/25/12)

Born, M. and Wolf, E. (1980) Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, Pergamon Press, New York (cited 8/28/12)

Bradley, R. C. (2005). Basic properties of strong mixing conditions. A survey and some open questions. Probab. Surv. 2 107–144 (electronic). (Update of, and a supplement to, the 1986 original.)  (cited 10/23/12)

Bullitt, E. and Aylward, S. (2002). Volume rendering of segmented image objects. IEEE Transactions on Medical Imaging, 21, 998-1002  (cited 10/29/12)

Cabanski, C. R., Qi, Y., Yin, X., Bair, E., Hayward, M. C., Fan, C., Li, J., Wilkerson, M. D., Marron, J. S., Perou, C. M. and Hayes, D. N. (2010) SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements, PLoS ONE, 5(3): e9905. doi:10.1371/journal.pone.0009905, PMCID: PMC2845619.   (cited 9/20/12)

Cardoso, J.F. and Souloumiac, A. (1993) Blind beamforming for non-Gaussian signals, Radar and Signal Processing, IEE Proceedings F, 140, 362-370 (cited 12/4/12)

Cates, J., Fletcher, P.T., Styner, M., Shenton, M., and Whitaker, R. T. (2007) Shape Modeling and Analysis with Entropy-Based Particle Systems, In Proceedings of Information Processing in Medical Imaging (IPMI) 2007, LNCS 4584, 333-345 (cited 10/25/12)

Chaudhuri, P. and Marron, J. S. (1999) SiZer for exploration of structure in curves, Journal of the American Statistical Association, 94, 807-823 (cited 8/30/12)

Cootes, T. F., Hill, A., Taylor, C. J. and Haslam, J. (1993) The use of active shape models for locating structures in medical images, Information in Medical Imaging, H. H. Barret and A. F. Gmitro, eds. Lecture Notes in Computer Science 687, 33-47, Springer Verlag, Berlin (cited 10/25/12)

Devijver, P. A. & Kittler, J. (1982) Pattern Recognition: A Statistical Approach, Prentice Hall, London  (cited 8/21/12)

Diaconis, P. & Freedman, D. (1984) Asymptotics of Graphical Projection Pursuit, Annals of Statistics, 12, 793-815 (cited 12/4/12)

Domingos, P. & Pazzani, M. (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103–­137 (cited 9/6/12)

Dryden, I.L., Mardia, K.V. (1998) Statistical Shape Analysis, Wiley, Chichester (cited 10/25/12)

Duda, R. O. and Hart P. E. (1973) Pattern Classification and Scene Analysis, Wiley, New York (cited 9/6/12)

Duda, R. O., Hart P. E. and Stork, D. G. (2001) Pattern Classification, Wiley, New York (cited 9/6/12)

El Karoui, N. (2010) The spectrum of kernel random matrices, Annals of Statistics, 38, 1-50 (cited 10/23/012)

Fan, J. & Gijbels, I. (1996) Local Polynomial Modelling and Its Applications, Chapman and Hall, London  (cited 8/30/12)

Fisher, N. I. (1983) Graphical Methods in Nonparametric Statistics: A Review and Annotated Bibliography, International Statistical Review, 51, 25-58  (cited 9/18/2012)

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Fisher, N. I. (1993) Statistical analysis of circular data, Cambridge University Press, Cambridge (cited 10/30/012)

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Hall, P., Marron, J. S. & Neeman, A. (2005) Geometric representation of high dimension, low sample size data, Journal of the Royal Statistical Society Series B, 67, 427-444  (cited 10/4/12)

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Hotz, H., Huckemann, S., Le, H., Marron, J. S., Mattingly, J. C., Miller, E., Nolen, J., Owen, M., Patrangenaru, V., and Skwerer, S. (2012) Sticky Central Limit Theorems on Open Books, arXiv:1202.4267 (cited 11/26/12)

Huckemann, S., Hotz, T. & Munk, A. (2010). Intrinsic shape analysis: Geodesic PCA for Riemannian manifolds modulo isometric lie group actions. Statistica Sinica, 20, 1–58 (cited 10/25/12)

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Jolliffe, I. T. (2002) Principal Component Analysis, Springer, New York, 2nd Edition, ISBN 978-0-387-95442-4   (cited 9/25/12)

Jones, M.C., Marron, J.S. & Sheather, S.J. (1996) A brief survey of bandwidth selection for density estimation, Journal of the American Statistical Association, 91, 401-407  (cited 8/30/12)

Jung, S., Dryden I. L., an Marron, J. S., (2012) Analysis of Principal Nested Spheres, Biometrika, doi: 10.1093/biomet/ass022 (cited 10/29/12)

Jung, S. X. Liu, X., Marron, J. S. and S. M. Pizer, S. M. (2010) Generalized PCA via the backward stepwise approach in image analysis, Brain, Body and Machine,Proceedings on an International Symposium on the Occasion of the 25th Anniversary of the McGill Centre for Intelligent Machines, Montreal, (J. Angeles, et al., eds.), Springer, New York, 111-123 (cited 10/29/12)

Jung, S. and Marron, J. S. (2009), PCA consistency in high dimension, low sample size context, The Annals of Statistics 37, 4104-4130  (cited 10/4/12)

Jung, S., Foskey, M. and J. S. Marron, J. S. (2011) Principal Arc Analysis on direct product manifolds, The Annals of Applied Statistics, 5, 578-603   (cited 11/1/12)

Jung, S., Sen, A. and Marron, J. S. (2012), Boundary behavior in high dimension, low sample size asymptotics of PCA, The Journal of Multivariate Analysis,109, 190–203  (cited 10/23/12)

Kaufman, L. and Rousseeuw, P. J. (2005) Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York  (cited 9/18/2012)

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Liu, Y., Hayes, D. N., Nobel, A. and Marron, J. S. (2008) Statistical Significance of Clustering for High Dimension Low Sample Size Data, Journal of the American Statistical Association, 103, 1281-1293  (cited 9/20/12)

Locantore, N., Marron, J. S., Simpson, D. G., Tripoli, N., Zhang, J. T. and Cohen, K. L. (1999) Robust PCA for Functional Data, Test, 8, 1-73 (cited 8/28/12)

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Marron, S. Jung, J. S. and Dryden, I. L. (2010) Speculation on the Generality of the Backward Stepwise View of PCA, Proceedings of MIR 2010: 11th ACM SIGMM International Conference on Multimedia Information Retrieval, Association for Computing Machinery, Inc., Danvers, MA, 227-230. (cited 10/30/12)

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Course Information:


        J. S. Marron, Professor




        Hanes Hall 352    (in back hall behind central open area)


        Office:    919-962-2188
        Home:    919-49302844

Office hours:

        When I am in my office (usually M, T, Th, priority to those with appointments)