- Software and data
- Summer School
- Mosler, K. and Mozharovskyi, P. (2017): Fast DD-classification of functional data. Statistical Papers, 58(4), 1055–1089. [arXiv:1403.1158]
- Mozharovskyi, P. and Vogler, J. (2016): Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples. Economics Letters, 148, 87–90. [SSRN-id2806151] [Matlab and C++ sources]
- Badunenko, O. and Mozharovskyi, P. (2016): Nonparametric frontier analysis using STATA. Stata Journal, 16(3), 550–589. [PDF] [STATA and C++ sources]
- Dyckerhoff, R. and Mozharovskyi, P. (2016): Exact computation of the halfspace depth. Computational Statistics and Data Analysis, 98, 19–30. [arXiv:1411.6927] [C++ sources]
- Mozharovskyi, P., Mosler, K., and Lange, T. (2015): Classifying real-world data with the DDα-procedure. Advances in Data Analysis and Classification, 9(3), 287–314. [arXiv:1407.5185]
- Lange, T., Mosler, K., and Mozharovskyi, P. (2014): Fast nonparametric classification based on data depth. Statistical Papers, 55(1), 49–69. [arXiv:1207.4992]
- Lange, T. and Mozharovskyi, P. (2010): Depth determination for multivariate samples (in Russian). Inductive Modelling of Complex Systems, I 2, 101–119.
- Grishko, V.F. and Mozharovsky, P.F. (2009): Management-information system hardware reliability evaluation (in Ukrainian). Mathematical Machines and Systems, 3, 194–201.
- Lange, T., Mosler, K., and Mozharovskyi, P. (2014): DDα-classification of asymmetric and fat-tailed data. In: Spiliopoulou, M., Schmidt-Thieme, L., and Janning, R. (eds.), Data Analysis, Machine Learning and Knowledge Discovery, Springer, Berlin, 71–78. [pdf]
- Lange, T. and Mozharovskyi, P. (2014): The alpha-procedure: a nonparametric invariant method for automatic classification of multi-dimensional objects. In: Spiliopoulou, M., Schmidt-Thieme, L., and Janning, R. (eds.), Data Analysis, Machine Learning and Knowledge Discovery, Springer, Berlin, 79–86. [pdf]
- Lange, T., Mosler, K., and Mozharovskyi, P. (2013): Efficient depth-based classification using a projective invariant of class membership (in Russian). Control Systems and Computers, 2, 47–58.
- Lange, T., Mozharovskyi, P., and Barath, G. (2011): Two approaches for solving tasks of pattern recognition and reconstruction of functional dependencies. Proceedings of ASMDA Conference, Rome, 7–10 June 2011 (supplemented with examples and benchmark results, Statistical Week, Leipzig, 19–23 September 2011).
- Rolick, A., Mozharovskyi, P., and Mart, B. (2010): Application of depth-trimmed regions in IT-infrastructure control systems (in Russian). Coll. of Papers of the 10th Int. Conf. Intellectual Analysis of Information, Kyiv, 18–21 May 2010, 214–221.
- Mozharovskyi, P (2015): Contributions to depth-based classification and computation of the Tukey depth. Dr. Kovač Verlag, Hamburg. [pdf]
Work in progress:
- Liu, X., Mosler, K., and Mozharovskyi, P. (2017): Fast computation of Tukey trimmed regions and median in dimension p>2. [arXiv:1412.5122] [R-package TukeyRegion]
- Mozharovskyi, P., Josse, J., and Husson, F. (2017): Nonparametric imputation by data depth. [arXiv:1701.03513] [R-package and experiments]
- Pokotylo, O., Mozharovskyi, P., and Dyckerhoff, R. (2016): Depth and depth-based classification with R-package ddalpha. [arXiv:1608.04109] (conditionally accepted for Journal of Statistical Software)
- Mozharovskyi, P. (2016): Tukey depth: linear programming and applications. [arXiv:1603.00069]
- Web-page with 50 ready-to-use real-data binary classification tasks:
- C++ sources for exact computation of the halfspace depth [C++ sources].
- Matlab and C++ sources for composite marginal likelihood estimation [Matlab and C++ sources].
- R-package and experiments reproducing scripts for imputation by data depth [R-package and experiments].
- R-package ddalpha.
- R-package npsf.
- R-package TukeyRegion.