Centre Henri Lebesgue Institut de recherche mathématique de Rennes Agrocampus Ouest
Pavlo Mozharovskyi My name is Pavlo Mozharovskyi and this website is about my professional activities. I am a postdoc researcher at Centre Henri Lebesgue in Rennes. My main research interests lie in nonparametric and computational statistics, classification, and imputation of missing data. Here is my CV.
  • 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
  • E-mail:    pavlo.mozharovskyi@univ-rennes1.fr
  • 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. [arXiv:1403.1158]
  • 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., 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.
PhD thesis:
  • Mozharovskyi, P (2015): Contributions to depth-based classification and computation of the Tukey depth. Dr. Kovač Verlag, Hamburg. [pdf]
Work in progress:
  • 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 ddalpha. [arXiv:1608.04109]
  • 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]
Software and data:
Copyright: Pavlo Mozharovskyi.   Last updated: August 17, 2016.