Object (oriented) data analysis

University of North Carolina at Chapel Hill

• https://scholar.google.com/citations?user=Ft9MrIoAAAAJ&hl=en
• http://marron.web.unc.edu/stor-881-object-oriented-data-analysis-fall-2019/
• http://marron.web.unc.edu/marron-dryden-ooda-book/
• J. S. Marron and Ian L. Dryden: Object Oriented Data Analysis. June 2020.
  https://marronwebfiles.sites.oasis.unc.edu/Teaching/OODA-STOR881-Fall2019/OODAbookV12.pdf

1. * H Wang, JS Marron: Object oriented data analysis: Sets of trees. The Annals of Statistics 35 (5), 1849-1873, 2007.
   https://projecteuclid.org/euclid.aos/1194461714
2. * JS Marron, AM Alonso: Overview of object oriented data analysis. Biometrical Journal 56 (5), 732-753, 2014.
   https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.201300072
3. * X Zhao, JS Marron, MT Wells: The functional data analysis view of longitudinal data. Statistica Sinica, 789-808, 2004.
   http://www3.stat.sinica.edu.tw/statistica/oldpdf/a14n38.pdf
4. * JS Marron, JO Ramsay, LM Sangalli, A Srivastava: Functional data analysis of amplitude and phase variation. Statistical Science, 468-484, 2015. https://www.jstor.org/stable/24780816?seq=1#metadata_info_tab_contents
5. F Hernandez-Campos, JS Marron, G Samorodnitsky, FD Smith: Variable heavy tails in internet traffic. Performance Evaluation 58 (2-3), 261-284, 2004.
6. PK Kimes, Y Liu, D Neil Hayes, JS Marron: Statistical significance for hierarchical clustering. Biometrics 73 (3), 811-821, 2017
7. F Hernández-Campos, JS Marron, G Samorodnitsky, FD Smith: Variable heavy tailed durations in internet traffic. Part i. Understanding heavy tails. Proceedings. 10th IEEE International Symposium on Modeling, Analysis and … 2002
8. D Shen, H Shen, S Bhamidi, Y Muñoz Maldonado, Y Kim, JS Marron: Functional data analysis of tree data objects. Journal of Computational and Graphical Statistics 23 (2), 418-438, 2014.
9. * B Aydın, G Pataki, H Wang, A Ladha, E Bullitt, JS Marron: Visualizing the structure of large trees. Electronic Journal of Statistics 5, 405-420, 2011. https://projecteuclid.org/euclid.ejs/1305034908
10. * I Carmichael, JS Marron: Data science vs. statistics: two cultures? Japanese Journal of Statistics and Data Science 1 (1), 117-138, 2018.
    https://link.springer.com/content/pdf/10.1007/s42081-018-0009-3.pdf
11. * J Hannig, JS Marron, R Riedi: Zooming statistics: Inference across scales. Journal of the Korean Statistical Society 30 (2), 327-345, 2001.
    https://hannig.cloudapps.unc.edu/publications/HannigMarronReidi2001.pdf
12. * Q Feng, J Hannig, JS Marron: A note on automatic data transformation. Stat 5 (1), 82-87, 2016.
    https://onlinelibrary.wiley.com/doi/epdf/10.1002/sta4.104
13. J Hannig, JS Marron, G Samorodnitsky, FD Smith: Log-normal durations can give long range dependence. Lecture Notes-Monograph Series, 333-344, 2003.
14. JS Marron: Distance‐weighted discrimination. Wiley Interdisciplinary Reviews: Computational Statistics 7 (2), 109-114, 2015.
15. * JS Marron: Big Data in context and robustness against heterogeneity. Econometrics and Statistics 2, 73-80, 2017.
    https://www.sciencedirect.com/science/article/pii/S2452306216300016
16. SM Pizer, JS Marron: Object statistics on curved manifolds. Statistical Shape and Deformation Analysis, 137-164, 2017.
17. * X Lu, JS Marron, P Haaland: Object-oriented data analysis of cell images. Journal of the American Statistical Association 109 (506), 548-559, 2014. https://www.tandfonline.com/doi/full/10.1080/01621459.2014.884503
18. H Huang, Y Liu, DN Hayes, A Nobel, JS Marron, C Hennig: Significance testing in clustering. Handbook of Cluster Analysis, 336-357, 2015.
19. * JS Marron, JR Wendelberger, EM Kober: Time series functional data analysis. Los Alamos National Lab, Release Number LA-UR-04 3911, 2004. https://marronwebfiles.sites.oasis.unc.edu/OldResearch/ChemoSpect/TimeSeriesFDA3.pdf
20. J Jiang, JS Marron, X Jiang: Robust centroid based classification with minimum error rates for high dimension, low sample size data. Journal of statistical planning and inference 139 (8), 2571-2580, 2009.
21. * X Lu, JS Marron: Analysis of juggling data: Object oriented data analysis of clustering in acceleration functions. Electronic Journal of Statistics 8 (2), 1842-1847, 2014. https://projecteuclid.org/euclid.ejs/1414588171
22. JS Marron: Object oriented data analysis: Open problems regarding manifolds. Contributions in Infinite-Dimensional Statistics and Related Topics, 185-190, 2014
23. JS Marron, AM Alonso: Rejoinder to the discussion of: Overview of object-oriented data analysis. Biometrical journal. Biometrische Zeitschrift 56 (5), 790, 2014
24. * R Izem, J Marron: Functional data analysis of nonlinear modes of variation. Electronic Journal of Statistics 1, 2007.
    https://projecteuclid.org/euclid.ejs/1197908969
25. * JE Chacón, T Duong, JE Chacón, T Duong, JE Chacón, T Duong, …: Escaping RGBland: Selecting colors for statistical graphics. Multivariate Kernel Smoothing and its Applications 10 (206), 1-9, 2018. https://www.sciencedirect.com/science/article/pii/S0167947308005549
26. JS Marron, 2014: Object Oriented Data Analysis.
27. * JS Marron: Comments on: Data science, big data and statistics. TEST 28 (2), 342-344, 2019.
    https://link.springer.com/article/10.1007/s11749-019-00644-8
28. * H Wang, JS Marron: An Introduction to Functional Data Analysis of Populations of Tree-structured Objects.
    http://www.cs.unc.edu/Research/MIDAG/pubs/papers/IPMI03_Wang.pdf
29. * Kehagias, A., Pitsoulis, L. Bad communities with high modularity. Eur. Phys. J. B 86, 330 (2013). https://doi.org/10.1140/epjb/e2013-40169-1 ; https://link.springer.com/content/pdf/10.1140/epjb/e2013-40169-1.pdf
30. * Severn, K. E., Dryden, I. L., Preston, S. P., & Severn, K. E. (2019). Discussion: Object-Oriented Data Analysis, Power Metrics, and Graph Laplacians. Journal of the American Statistical Association, 114(527), 1097-1098.
    https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1635477 ; https://doi.org/10.1080/01621459.2019.1635477
31. * Casal AR.: Comments on: Overview of object oriented data analysis. Biom J. 2014 Sep;56(5):768-70. doi: 10.1002/bimj.201300191. Epub 2014 Jun 5. https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.201300191
32. * Quintero FO, Porcu E.: Extensions of object oriented data analysis. Biom J. 2014 Sep;56(5):764-5. doi: 10.1002/bimj.201300181. Epub 2014 Mar 7. https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.201300181
33. * Sangalli LM, Secchi P, Vantini S.: Object Oriented Data Analysis: A few methodological challenges. Biom J. 2014 Sep;56(5):774-7. doi: 10.1002/bimj.201300217. Epub 2014 Apr 17. https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.201300217
34. * Victor Patrangenaru, Leif Ellingson: Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis [1 ed.]. Chapman & Hall/CRC Monographs on Statistics & Applied Probability
35. * Ian L. Dryden, Kanti V. Mardia: Statistical Shape Analysis: With Applications in R [2ed.] Wiley Series in Probability and Statistics.
36. * Anuj Srivastava, Eric P. Klassen: Functional and Shape Data Analysis [1 ed.]. Springer Series in Statistics

Contributions in infinite-dimensional statistics and related topics By Enea G. Bongiorno, Ernesto Salinelli, Aldo Goia, Philippe Vieu