name is Pavlo Mozharovskyi and this website is about my
professional activities. I am a postdoc researcher at
Lebesgue in Rennes. My main research interests lie in
nonparametric and computational statistics, classification, and
imputation of missing data. Here is my
- Address: Room 24-0-46, Applied
Mathematics Unit, Agrocampus Ouest
65 rue de Saint-Brieuc
35042 Rennes, France
- Phone: +33 (0) 2 23
48 54 76
- Badunenko, O. and Mozharovskyi, P. (2016): Nonparametric
frontier analysis using STATA. Stata Journal, to appear.
- Mosler, K. and Mozharovskyi, P. (2015): Fast DD-classification
of functional data. Statistical Papers, to appear.
- 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),
- 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., 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, 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).
- Lange, T. and Mozharovskyi, P. (2010): Depth
determination for multivariate samples (in Russian).
Inductive Modelling of Complex Systems, I 2, 101–119.
- 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.
- Grishko, V.F. and Mozharovsky, P.F. (2009):
Management-information system hardware reliability
evaluation (in Ukrainian). Mathematical Machines and
Systems, 3, 194–201.
- Mozharovskyi, P (2015): Contributions to depth-based
classification and computation of the Tukey depth. Dr.
Kovač Verlag, Hamburg. [pdf]
- Mozharovskyi, P. and Vogler, J. (2016): Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples. [SSRN-id2806151]
- Pokotylo, O., Mozharovskyi, P., and Dyckerhoff, R. (2016): Depth and
depth-based classification with R-package
- Mozharovskyi, P. (2015): Tukey depth: linear programming
and applications. [arXiv:1603.00069]
- Liu, X., Mosler, K., and Mozharovskyi, P. (2015): Fast
computation of Tukey trimmed regions in dimension p>2. [arXiv:1412.5122]