Course Home Page – STOR 881 Object Oriented Data Analysis

• Latest Draft of Marron & Dryden Book:  Object Oriented Data Analysis (draft Aug. 18, 2017)

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

Software:

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

References:

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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)

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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)

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

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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)

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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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Huang, H., Liu, Y., Yuan, M. and Marron J.S. (2014) Statistical Significance of Clustering Using Soft Thresholding, Journal of Computational and Graphical Statistics, DOI:10.1080/10618600.2014.948179 (cited 10/31/2017)

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Jung, S., Liu, X., Marron, J. S., & Pizer, S. M. (2010). Generalized PCA via the backward stepwise approach in image analysis. In Brain, Body and Machine (pp. 111-123). Springer Berlin Heidelberg (cited 11/14/17)

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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Kimes, P. K., Cabanski, C. R., Wilkerson, M. D., Zhao, N., Johnson, A. R., Perou, C. M., Makowski, L., Marron, J. S., Hayes, D. N. (2014) SigFuge: single gene clustering of RNA-seq reveals differential isoform usage among cancer samples, Nucleic Acids Research (2014): gku521 (cited 8/24/17)

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Koch, I., Hoffmann, P., & Marron, J. S. (2014). Proteomics profiles from mass spectrometry. Electronic Journal of Statistics, 8(2), 1703-1713 (11/28/17)

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Lu, X., & Marron, J. S. (2014). Analysis of juggling data: Object oriented data analysis of clustering in acceleration functions. Electronic Journal of Statistics, 8(2), 1842-1847 (cited 11/28/17)

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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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Pizer, S. M., Jung, S., Goswami, D., Vicory, J., Zhao, X., Chaudhuri, R., … & Marron, J. S. (2013). Nested sphere statistics of skeletal models. In Innovations for Shape Analysis (pp. 93-115). Springer Berlin Heidelberg (11/14/17)

Qiao, X., Zhang, H. H., Liu, Y., Todd, M. J., & Marron, J. S. (2010). Weighted distance weighted discrimination and its asymptotic properties. Journal of the American Statistical Association, 105(489), 401-414 (cited 10/5/17)

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Rondonotti, V., Marron, J. S., & Park, C. (2007). SiZer for time series: a new approach to the analysis of trends. Electronic Journal of Statistics, 1, 268-289 (cited 10/26/17)

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