Publications and preprints

1. Hyodo M. Nishiyama T. and Pavlenko T. (2020): Testing independence in high-dimensional data: ρV-coefficient based approach. Journal of Multivariate Analysis. Link
2. Hyodo M. Nishiyama T. and Pavlenko T. (2020): On error bounds for high-dimensional asymptotic distribution of L_2-type test statistic for equality of means. Statistics & Probability Letters. Link
3. Olsson J, Pavlenko T. and Rios F. (2019): Bayesian learning of weakly structural Markov graph laws using sequential Monte Carlo methods. Electronic Journal of Statistics. Link
4. Ahmad R. M. Pavlenko T. (2018): A U-classifier for high-dimensional data under non-normality. Journal of Multivariate Analysis. Link
5. Stepanova N, Pavlenko T. (2018): Goodness-of-fit tests based on sup-functionals of weighted empirical processes. SIAM Journal on Theory of Probability and Its Applications (TVP), Link
6. Pavlenko T. Rios F. (2018): Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs. Link Under review.
7. Olsson J, Pavlenko T. and Rios F. (2018): Sequential sampling of junction trees for decomposable graphs. Link Under review.
8. Pavlenko T. Roy A. (2017): Supervised classifiers of high-dimensional higher-order data with locally doubly exchangeable covariance structure. Communications in Statistics - Theory and Methods. Link
9. Gauraha N, Pavlenko T. and Parui S. (2017): Post-Lasso stability selection for high-dimensional linear models. ICPRAM Link
10. Pavlenko T. Roy A. (2016): Performance accuracy of linear classifiers for two-level multivariate observations in high-dimensional framework. Link
11. Hyodo M, Shutoh N, Seo T and Pavlenko T. (2016): Estimation of high-dimensional covariance matrices with two-step monotone missing data. Communications in Statistics - Theory and Methods, 7: 1910--1922. Link
12. Hyodo M, Shutoh N, Nishiyama T, Pavlenko T. (2015): Testing block-diagonal covariance structure for high-dimensional data. Statistica Neerlandica. Link
13. Watanabe H, Hyodo M, Seo T and Pavlenko T. (2015): Asymptotic properties of the misclassification errors for Euclidean distance discriminant rule in high-dimensional data. Journal of Multivariate Analysis, 140: 234-244. Link
14. Koizumi K, Sumikawa T, and Pavlenko T. (2014): Measures of multivariate skewness and kurtosis in high-dimensional framework. SUT Journal of Mathematics, 50(2): 483-511. Link
15. Koizumi K, Hyodo M, and Pavlenko T. (2014): Modified Jarque-Bera type tests for multivariate normality in a high-dimensional framework. Journal of Statistical Theory and Practice, 8(2): 382-399. Link
16. Takahashi S, Hyodo M, Nishiyama T, Pavlenko T. (2013): Multiple comparison procedures for high-dimensional data and their robustness under non-normality. Journal of Japanese Society of Computational Statistics, 26: 71-82. Link
17. Nishiyama T, Hyodo M, Seo T and Pavlenko T. (2013): Testing linear hypotheses of mean vector for high-dimensional data with unequal covariance matrices. Journal of Statistical Planning and Inference, 143: 1898-1911. Link
18. Corander J, Koski T, Pavlenko T and Tillander A. (2013): Bayesian block-diagonal predictive classifier for Gaussian data. Book Ch. in: Synergies of Soft Computing and Statistics for Intelligent Data Analysis}, Kruse R., Berthold M., Moewes C., Gil M., Grzegorzewski P., Hryniewicz O. (Eds). 190: 543-555, Springer. Link
19. Pavlenko T, Björkström A and Tillander A. (2012): Covariance structure approximation via gLasso in high-dimensional supervised classification. Journal of Applied Statistics, 8, 1643-1666. Link
20. Shutoh N, Hyodo M, Pavlenko T and Seo T. (2012): Constrained linear discriminant rule via the Studentized classification statistic based on monotone missing data. SUT Journal of Mathematics, 48(1), 55-69.
21. Fomina S, Pavlenko T, and Bagdasarova I. (2011): Steroid-resistant nephrotic syndrome in childhood: single-center experience. Clinical nephrology, 3, 65-69.
22. Fomina S, Pavlenko T, Englund E and Bagdasarova I. (2011): Clinical Course of Steroid Sensitive Nephrotic Syndrome in Children: Outcome and Outlook. Pediatric Medicine Journal, 5, 18-28.
23. Fomina S, Pavlenko T, Bagdasarova I (2010): Survival functions in steroid resistant nephrotic syndrome in children. Actual Problems of Nephrology (in Ukrainian), 16, 135-147.
24. Fomina S, Pavlenko T, Englund E and Bagdasarova I. (2010): Clinical Patterns and Renal Survival of Nephrotic Syndrome in Childhood: A Single-Center Study (1980-2006). Urology & Nephrology Journal, 3, 8-15.
25. Pavlenko T and Björkström A. (2010): Exploiting sparse dependence structure in model-based classification. Book Ch. in: Advances in Intelligent and Soft Computing, Borgelt C. et al. (eds), 77, 510-517, Springer. Link
26. Appelberg, J, Janson, C, Lindberg, E, Pavlenko, T and Hedenstierna, G. (2010): Lung aeration during sleep in patients with obstructive sleep apnoea. Clinical Physiology and Functional Imaging, , 30, 301-307. Link
27. Pavlenko T, Chernyak O. (2010): Credit risk modeling using Bayesian Networks. Journal of Intelligent Systems, 25(4), 326-344. Link
28. Appleberg J, Pavlenko T, Bergman H, Rothen HU and Hedenstierna G. (2007): Lung aeration during sleep. Chest, 131, 122-129. Link
29. Pavlenko, T and Fridén, H. (2006): Scoring feature subsets for separation power in supervised Bayes classification. Book Ch. in: Soft Methods for Integrated Uncertainty Modelling, 37, 383-391, Springer. Link
30. Pavlenko T and von Rosen D.(2005): On the optimal weighting of high-dimensional Bayesian networks. Advances and Applications in Statistics, 4, 357-377.
31. Dahmoun, M, Ödmark, IS, Risberg, B, Pavlenko, T and Bäckström, T. (2004): Apoptosis, proliferation and sex steroid receptors in postmenopausal endometrium before and during HRT. Maturitas, 49(2), 114-23. Link
32. Pavlenko T, Hall M, von Rosen D. and Andrushchenko Z. (2004): Towards the optimal feature selection in high-dimensional Bayesian network classifiers. Book Ch. in: Soft Methodology and Random Information Systems, 4, 613-620, Springer. Link
33. Pavlenko T. (2003): Feature informativeness in high-dimensional discriminant analysis. Communications in Statistics: Theory and Methods, 32, 459-474. Link
34. Pavlenko T. (2003): On feature selection, curse-of-dimensionality and error probability in discriminant analysis.Journal of Statistical Planning and Inference , 115: 565-584. Link
35. Pavlenko T and von Rosen D.(2002): Bayesian networks classifiers in high-dimensional framework. Book Ch. in: Uncertainty in Artificial Intelligence, 397-404, Morgan Kaufmann Publishers, San Francisco California. Link
36. Pavlenko T and von Rosen D. (2001): Effect of dimensionality on discrimination. Statistics: Journal of Theoretical and Applied Statistics, 35(3), 191-213.
37. Dorfman M, Ganul V, Girko V and Pavlenko T. (1990): Questionnaire-based determination of groups at high risk for lung cancer (Russian, English sumamry). Problems of Oncology, 36(12), 1469-1474.
38. Girko, V and Pavlenko T. (1989): 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, 61-64.
39. Girko, V and Pavlenko T. (1989): G-estimates of the quadratic discriminant function. (Russian, English summary). Ukrainian Mathematical Journal, 41(12), 1469-1473.
40. Pavlenko T. (1989): 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, 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.