Papers

1. Nishiyama T, Hyodo M, Seo T and Pavlenko T. Testing linear hypotheses of mean vector for high-dimensional data with unequal covariance matrices, (2013). Submitted.
2. Hyodo M, Shutoh N, Seo T and Pavlenko T. Estimation of high-dimensional covariance matrices with two-step monotone missing data, (2012). Under review.
3. Koizumi K, Hyodo M, and Pavlenko T. Modified Jarque-Bera type tests for multivariate normality in a high-dimensional framework, (2012). Under review.
4. Takahashi S, Hyodo M, Nishiyama T, Pavlenko T. Multiple comparison procedures for high-dimensional data and their robustness under non-normality. (2012). Under review.
5. Pavlenko T. Roy A. Supervised classifiers of ultra high-dimensional higher-order data with locally doubly exchangeable covariance structure, (2012). Under review.
6. Corander J, Koski T, Pavlenko T and Tillander A. Bayesian Block-diagonal Predictive Classifier for Gaussian data. Advances in Intelligent and Soft Computing, 190 (2012), 543-555.
7. Pavlenko T, Björkström A and Tillander A. Covariance structure approximation via gLasso in high-dimensional supervised classification. Journal of Applied Statistics, 8 (2012), 1643-1666.
8. Shutoh N, Hyodo M, Pavlenko T and Seo T. Constrained linear discriminant rule via the Studentized classification statistic based on monotone missing data. SUT Journal of Mathematics, 48(1) (2012), 55-69.
9. Fomina S, Pavlenko T, and Bagdasarova I. Steroid-resistant nephrotic syndrome in childhood: single-centre experience. Clinical nephrology, 3 (2011), 65-69.
10. Fomina S, Pavlenko T, Englund E and Bagdasarova I. Clinical Course of Steroid Sensitive Nephrotic Syndrome in Children: Outcome and Outlook. Pediatric Medicine Journal, 5 (2011), 18-28.
11. Fomina S, Pavlenko T, Bagdasarova I . Survival functions in steroid resistant nephrotic syndrome in children. Actual Problems of Nephrology (in Ukrainian), 16 (2010), 135-147.
12. Fomina S, Pavlenko T, Englund E and Bagdasarova I. Clinical Patterns and Renal Survival of Nephrotic Syndrome in Childhood: A Single-Center Study (1980-2006). The Open Urology & Nephrology Journal, 3 (2010), 8-15.
13. Pavlenko T and Björkström A. Exploiting sparse dependence structure in model based classification.Advances in Intelligent and Soft Computing, 77 (2010), 510-517.
14. Appelberg, J, Janson, C, Lindberg, E, Pavlenko, T and Hedenstierna, G. Lung aeration during sleep in patients with obstructive sleep apnoea. Clinical Physiology and Functional Imaging, , 30 (2010), 301-307.
15. Pavlenko T and Chernyak O. Credit risk modeling using Bayesian Networks. Journal of Intelligent Systems, 25(4) (2010), 326-344.
16. Appleberg J, Pavlenko T, Bergman H, Rothen HU and Hedenstierna G. Lung aeration during sleep. Chest, 131 (2007), 122-129.
17. Pavlenko, T and Fridén, H. Scoring feature subsets for separation power in supervised Bayes classification. Advances in Intelligent and Soft Computing, 37} (2006), 383-391.
18. Pavlenko T and von Rosen D. On the optimal weighting of high-dimensional Bayesian networks. Advances and Applications in Statistics, 4 (2005), 357-377.
19. Dahmoun, M, Ödmark, IS, Risberg, B, Pavlenko, T and Bäckström, T. Apoptosis, proliferation and sex steroid receptors in postmenopausal endometrium before and during HRT. Maturitas, 49(2) (2004), 114-23.
20. Pavlenko T, Hall M, von Rosen D. and Andrushchenko Z. Towards the optimal feature selection in high-dimensional Bayesian network classifiers. Soft Methodology and Random Information Systems, (2004), 613-620.
21. Pavlenko T. Feature informativeness in high-dimensional discriminant analysis. Communications in Statistics: Theory and Methods, 32 (2003), 459-474.
22. Pavlenko T. On feature selection, curse-of-dimensionality and error probability in discriminant analysis. Journal of statistical planning and inferences , 115 (2003), 565-584.
23. Pavlenko T and von Rosen D.Bayesian networks classifers in high-dimensional framework. In UAI18, Morgan Kaufmann publ. (2002), 397-404.
24. Pavlenko T and von Rosen D. Effect of dimensionality on discrimination. Statistics: Journal of Theoretical and Applied Statistics, 35 (3) (2001), 191-213.
25. Dorfman M, Ganul V, Girko V and Pavlenko T. Questionnaire-based determination of groups at high risk for lung cancer (Russian, English sumamry). Problems of Oncology 36(12) (1990), 1469-1474.
26. Girko, V and Pavlenko T. G-estimator of the regularized Mahalanobis distance in the case where the distribution of observations is different from the normal one. Dokl. AkadNaukUkrSSR, Ser.A, (Russian English summary), 11 (1989), 61-64.
27. Girko, V and Pavlenko T. G-estimates of the quadratic discriminant function. (Russian, English summary). Ukrainian Mathematical Journal, 41(12) (1989), 1469-1473.
28. Pavlenko T. G-estimation of the Mahalanobis distance for the case of an arbitrary continuous distribution of observed vectors. (Russian. English summary). Trydu Tartu Vychisl Tsentr, 56 (1988), 50-58.

Compendium ets

• Pavlenko T. Introduction to Probability and Statistics. Compendium for the introductory course in probability and statistics. Department of Engineering, Physics and Mathematics, Mid Sweden University, 2003.
• Pavlenko T. Variable informativeness in discriminant analysis. Serie: Department of Mathematical Statistics, Lund University, Lund Institute of Technology, 1403-6207 1998:7.
• Pavlenko T. and von Rosen D. Estimating the discriminant function when the number of variables is large. Serie: U.U.D.M, 1101-3591, 1996:6.

Theses

• Pavlenko T. Methods of statistical classification of high-dimensional data with applications. PhD Theses. Department of Applied Statistics, Kiev State University, Ukraine, 1991.
• Pavlenko T. Discriminant analysis with growing dimension. Licentiate Theses, Department of Mathematics, Division of Mathematical statistics, Uppsala University, 1997.
• Pavlenko T. Feature informativeness, curse-of-dimensionality and error probability in discriminant analysis. PhD Thesis. Department of Mathematical Statistics, Lund University, 2001.