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Course Notes:

  1.   STOR881-08-22-2017:   Organizational Matters, OODA Book, What is OODA?,  Taste of OODA Examples, Visualization, Scatterplot Matrix Views, Principal Component Analysis (PCA)
  2.   STOR881-08-24-2017:   OODA Basics, Data Object Determination & Representation, Object & Descriptor Spaces, 2-d toy Example, Curves as Data 10-d & 50-d, RNAseq Data, Revisit Mortality Data
  3.   STOR881-08-29-2017:   Correlation PCA, Limitations of PCA, NCI60 data, Marginal Distribution Plots, Start Drug Discovery Data
  4.   STOR881-08-31-2017:   Continue Drug Discovery Data, Marron’s Matlab Software, DiProPerm Hypothesis Test
  5.   STOR881-09-05-2017:   Melanoma Data, Transformations,  Revisit Drug Discovery Data,  Yeast Cell Cycle Data & Fourier Subspace
  6.   STOR881-09-07-2017:   Review of Linear Algebra & Multivariate Probability, PCA as Optimization, Redistribution of Energy
  7.   STOR881-09-12-2017:   Different Views of PCA, Data Representation –  Simulation – Visualization, Dual PCA & Mortality Data, Cornea Data & Robustness
  8.   STOR881-09-14-2017:   Cornea Data, Robustness: Center & PCA, Spherical PCA, Elliptical PCA
  9.   STOR881-09-19-2017:   GWAS Analysis, Classification: Fisher Linear Discrimination, Gaussian Likelihood Ratio, Mean Difference
  10.   STOR881-09-21-2017-part1, STOR881-09-21-2017-part2, STOR881-09-21-2017-part3:   HDLSS Discrimination, Maximal Data Piling
  11.   STOR881-09-26-2017:    Kernel Embedding, Support Vector Machine, Distance Weighted Discrimination, Faces Data
  12.   STOR881-09-28-2017:    DWD Simulations, Batch Adjustment, HDLSS Asymptotics – Jonathan Williams {Bayesian HMM}, Ruibin Ma {Generalized Cylindrical Surface Deformation}
  13.   STOR881-10-03-2017:    Why DWD for Batch Adjustment, HDLSS Asymptotics – Yunxiao Liu {Integrated Volatility Functionals}
  14.   STOR881-10-05-2017:     HDLSS Asymptotics – Jack Prothero {Image Textures}
  15.   STOR881-10-10-2017:    Meilei Jiang {Angle Based Joint & Individual Variation Explained}
  16.   STOR881-10-12-2017:    University Day – No Class
  17.   STOR881-10-17-2017:    Radial DWD, Melanoma Data & ROC curves, Introduction to Clustering – Zhenlin Xu {Introduction to 3D deep learning}, Dylan Glotzer {Extreme Ship Motions}
  18.   STOR881-10-19-2017:    Fall Break
  19.   STOR893-10-24-2017:    Statistical Smoothing – Brendan Brown, Chen Shen, Wesley Hamilton {Topological Data Analysis)
  20.   STOR881-10-26-2017:    SiZer for Inference and Analysis of Mass Flux & Cell Cycle Data, Clustering, K-means, SWISS – Duyeol Lee {PCA in Credit Risk Modelling}
  21.   STOR881-10-31-2017:    Hierarchical Clustering, SigClust, QQ Plots, QQ Envelope – Kevin Donovan {Non-parametric inference for immune response thresholds of risk in vaccine studies}, Matt Jansen {Text Mining}
  22.   STOR881-11-02-2017:    SigClust, Shapes as Data Objects – Aniish Sridhar {Analytics Competition}, Aditya Balaram {Single Pass PCA}
  23.   STOR881-11-07-2017:    Landmark Based Shape, Equivalence Relations, Quotient Spaces, Shape Representations, Male Pelvis Data & S-Reps – Gang Li {Boosting Methods}, Peiyao Wang {Sparse gradient learning}, Michael Conroy {Regularized PCA}
  24.   STOR881-11-09-2017:     Manifold Data Analysis, Principal Nested SpheresBackwards PCA – Mark He {Commuting networks amongst US counties}, Adam Waterbury {Reproducing Kernels for FDA}
  25.   STOR881-11-14-2017:    Backwards PCA, Nonnegative Matrix Factorization – Aman Barot {Introduction to Deep learning}, Pooja Saha {LASSO regression}, Yue Jiang {CART}
  26.   STOR881-11-16-2017:    Nested Constraints, Principal Nested Submanifolds – Shengjie Chai {Cancer Metastesis}, Di Qin {Kernel PCA}, Yaoyu Chen {Introduction to Generative Adversarial Networks}
  27.   STOR881-11-21-2017:    Curve Registration, Fisher Rao Approach,  – Xi Yang {Multi-View Weighted Network}, Hang Yu {Introduction to multiple kernel learning}, Zhipeng Ding {Fast Predictive Simple Geodesic Regression}
  28.   STOR881-11-23-2017:    Thanksgiving
  29.   STOR881-11-28-2017:    Curve Registration, TIC Data, PNS Approach, Juggling Data Yumeng Wang {Efficacy Analysis}, Jiawei Xu {Childbirth and breast cancer risk}
  30.   STOR881-11-30-2017:    Probability Distributions as Data Objects, Random Matrix Theory,  Zhengling Qi {Classification in personalized medicine}, Zhiyuan Liu {CPNS Visualization in Pablo}, Fuhui Fang {DiProPerm Analysis of OsteoArthritis Data}
  31.   STOR881-12-05-2017-part1, STOR881-12-05-2017-part2:    Tree Structured Data Objects


Link to Marron’s Matlab Software (.zip file, expand to 4 directories, and put those in Matlab Path)

LungCancer2011.m for Analysis of 2011 RNAseq Lung Cancer Data (you need to remove suffix “.txt” from file name)

counts, for 2011 RNAseq Lung Cancer Data

exonsMarron, for 2011 RNAseq Lung Cancer Data

Single .zip file with above 3, plus generated graphics


Ahn, J. (2006) High dimension, low sample size data analysis. PhD Dissertation, University of North Carolina, Chapel Hill (cited 9/26/17)

Ahn, J., Marron, J. S., Muller, K. M., & Chi, Y. Y. (2007) The high-dimension, low-sample-size geometric representation holds under mild conditions. Biometrika, 94(3), 760-766 (cited 10/5/17)

Ahn, J., & Marron, J. S. (2010) The maximal data piling direction for discrimination. Biometrika, 97(1), 254-259 (cited 9/21/17)

Aizerman, A., Braverman, E. M., & Rozoner, L. I. (1964) Theoretical foundations of the potential function method in pattern recognition learning. Automation and remote control, 25, 821-837 (cited 9/21/17)

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Aydin, B., Pataki, G., Wang, H., Bullitt, E., & Marron, J. S. (2009). A principal component analysis for trees. The Annals of Applied Statistics, 1597-1615  (cited 12/05/17)

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Bendich, P., Marron, J. S., Miller, E., Pieloch, A., & Skwerer, S. (2016). Persistent homology analysis of brain artery trees. The annals of applied statistics, 10(1), 198  (cited 12/05/17)

Benito, M., Parker, J., Du, Q., Wu, J., Xiang, D., Perou, C. M., & Marron, J. S. (2004) Adjustment of systematic microarray data biases. Bioinformatics, 20(1), 105-114 (9/28/17)

Benito, M., García‐Portugués, E., Marron, J. S., & Peña, D. (2017). Distance‐weighted discrimination of face images for gender classification. Stat, 6(1), 231-240 (cited 9/26/17)

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Borysov, P., Hannig, J., Marron, J. S., Muratov, E., Fourches, D., & Tropsha, A. (2016). Activity prediction and identification of mis‐annotated chemical compounds using extreme descriptors. Journal of Chemometrics, 30(3), 99-108 (cited 8/31/17)

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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 10/26/17, 10/31/17)

Cai, T., Liu, W., & Xia, Y. (2014). Two‐sample test of high dimensional means under dependence. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 349-372 (cited 8/31/17)

Carmichael, I., & Marron, J. S. (2017). Geometric Insights into Support Vector Machine Behavior using the KKT Conditions. arXiv preprint arXiv:1704.00767 (cited 9/26/17, 10/3/17)

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Chaudhuri, P. and Marron, J. S. (1999) SiZer for exploration of structure in curves, Journal of the American Statistical Association, 94, 807-823 (cited 10/26/17)

Chaudhuri, P., & Marron, J. S. (2000). Scale space view of curve estimation. Annals of Statistics, 408-428 (cited 10/26/17)

Chen, S. X., & Qin, Y. L. (2010). A two-sample test for high-dimensional data with applications to gene-set testing. The Annals of Statistics, 808-835  (cited 8/31/17)

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 11/7/17)

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Dai, W., & Genton, M. G. (2016). Directional outlyingness for multivariate functional data. arXiv preprint arXiv:1612.04615 (cited 9/14/17)

Dai, W., & Genton, M. G. (2017). Multivariate Functional Data Visualization and Outlier Detection. arXiv preprint arXiv:1703.06419. (cited 9/14/17)

Damon, J., & Marron, J. S. (2014). Backwards principal component analysis and principal nested relations. Journal of Mathematical Imaging and Vision, 50(1-2), 107-114 (cited 11/16/17)

DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44, 837-845 (cited 10/17/17)

Dobriban, E. (2015). Efficient computation of limit spectra of sample covariance matrices. Random Matrices: Theory and Applications, 4(04), 1550019 (cited 11/30/17)

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Eltzner, B., Jung, S., & Huckemann, S. (2015). Dimension reduction on polyspheres with application to skeletal representations. In International Conference on Networked Geometric Science of Information (pp. 22-29). Springer, Cham. (cited 11/14/17)

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Feng, Q., Hannig, J., & Marron, J. S. (2016). A note on automatic data transformation. Stat, 5(1), 82-87  (cited 8/31/2017)

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Godtliebsen, F., Marron, J. S., & Chaudhuri, P. (2002). Significance in scale space for bivariate density estimation. Journal of Computational and Graphical Statistics, 11(1), 1-21 (cited 10/26/17)

Godtliebsen, F., Marron, J. S., & Chaudhuri, P. (2004). Statistical significance of features in digital images. Image and Vision Computing, 22(13), 1093-1104 (cited 10/26/17)

Godtliebsen, F., Marron, J. S., & Pizer, S. M. (2002). Significance in scale-space for clustering. Spatial clustering modeling. Chapman and Hall/CRC, 24-36 (cited 10/26/17)

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Hannig, J., & Marron, J. S. (2006). Advanced distribution theory for SiZer. Journal of the American Statistical Association, 101(474), 484-499 (cited 10/26/17)

Hannig, J., Marron, J. S., & Riedi, R. (2001). Zooming statistics: Inference across scales. Journal of the Korean Statistical Society, 30(2), 327-345 (cited 10/26/17)

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Jung, S., Foskey, M., & Marron, J. S. (2011). Principal arc analysis on direct product manifolds. The Annals of Applied Statistics, 578-603 (cited 11/9/17)

Jung, S., Dryden I. L., & Marron, J. S., (2012) Analysis of Principal Nested Spheres, Biometrika, doi: 10.1093/biomet/ass022 (cited 11/9/17)

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 10/5/17)

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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 10/31/17)

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Marron, J. S. & Alonso, A. M. (2014) Overview of object oriented data analysis, Biometrical Journal, 56, 732-753 (cited 8/22/17, 8/24/17)

Marron, J. S., Ramsay, J. O., Sangalli, L. M., & Srivastava, A. (2014). Statistics of time warpings and phase variations. Electronic Journal of Statistics, 8(2), 1697-1702 (cited 11/28/17)

Marron, J. S., Ramsay, J. O., Sangalli, L. M., & Srivastava, A. (2015). Functional data analysis of amplitude and phase variation. Statistical Science, 30(4), 468-484 (cited 11/28/17)

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Miao, D. (2015) Class-Sensitive Principal Components Analysis , UNC PhD Dissertation, (cited 9/21/17)

Miedema, J., Marron, J. S., Niethammer, M., Borland, D., Woosley, J., Coposky, J. & Thomas, N. E. (2012). Image and statistical analysis of melanocytic histology. Histopathology, 61(3), 436-444 (cited 9/5/17, 10/17/17)

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