1. Dong, A. and ​Melnykov, V. (2024) Contaminated Kent Mixture Model for Clustering Non-Spherical Directional Data with Heavy Tails or Scatter, accepted by Statistics and Probability Letters.

2. Zhang, Y., ​Melnykov, V., and Melnykov, I. (2023) On Model-Based Clustering of Directional Data with Heavy Tails, accepted by Journal of Classification.

3. Dong, A., ​Melnykov, V., Wang, Y., and Zhu, X. (2023) Conditional mixture modeling for heavy-tailed and skewed data, accepted by Stat.

4. ​Melnykov, V., Wang, Y., Melnykov, Y., Torti, F., Perrotta, D. and Riani, M. (2023) On Simulating Skewed and Cluster-Weighted Data for Studying Performance of Clustering Algorithms, accepted by Journal of Computational and Graphical Statistics.

​5. Melnykov, V. and Wang, Y. (2023) Conditional Mixture Modeling and Model-Based Clustering, Pattern Recognition, 133, 108994.

6. Tomarchio, S., Ingrassia, S. and Melnykov, V. (2022) Modeling Students’ Career Indicators via Mixtures of Parsimonious Matrix-Normal Distributions, Australian & New Zealand Journal of Statistics, 64:2, 117-132.

7. Melnykov, Y., Perry, M. and Melnykov, V. (2022) Robust Estimation of Multiple Change Points in Multivariate processes, Innovations in Multivariate Statistical Modelling: Navigating Theoretical and Multidisciplinary Domains, ed. Bekker, A., Ferreira, J., Arashi, M., and Chen, D., Springer.

8. Zhu, X., Sarkar, S., and Melnykov, V. (2022) MatTransMix: An R Package for Matrix Model-Based Clustering and Parsimonious Mixture Modeling, Journal of Classification, 39:1, 147-170.

9. Zhang, Y., Melnykov, V., and Melnykov, I. (2021) Semi-Supervised Clustering of Time-Dependent Categorical Sequences with Application to Discovering Education-Based Life Patterns, Statistical Modelling, 22:5.

10. Melnykov, V., Sarkar, S., and Melnykov, Y. (2021) On Finite Mixture Modeling and Model-Based Clustering of Directed Weighted Multilayer Networks, Pattern Recognition, 112, 107641.

11. Melnykov, Y., Zhu, X., and Melnykov, V. (2021) Transformation Mixture Modeling for Skewed Data Groups with Heavy Tails and Scatter, Computational Statistics, 36:1, 61-78.

12. Zhang, Y., Melnykov, V., and Zhu, X. (2021) Model-Based Clustering of Time-Dependent Categorical Sequences with Application to the Analysis of Major Life Event Patterns, Statistical Analysis and Data Mining, 14:3, 230-240.

13. Sarkar, S., Melnykov, V., and Zhu, X. (2021) Tensor-Variate Finite Mixture Modeling for the Analysis of University Professor Remuneration, Annals of Applied Statistics, 15:2, 1017-1036.

14. Sarkar, S., Melnykov, V., and Zheng, R. (2020) Gaussian Mixture Modeling and Model-Based Clustering Under Measurement Inconsistency, Advances in Data Analysis and Classification, 14:2, 379-413.

15. Michael, S., Miljkovic, T., and Melnykov, V. (2020) Mixture Modeling of Data with Multiple Partial Right-Censoring Levels, Advances in Data Analysis and Classification, 14:2, 355-378.

16. Wang, Y. and Melnykov, V. (2020) On Variable Selection in Matrix Mixture Modeling, Stat, 9:1.

17. Melnykov, I. and Melnykov, V. (2020) A Note on the Formal Implementation of the K-Means Algorithm with Hard Positive and Negative Constraints, Journal of Classification, 37:3, 789-809.

18. Sarkar, S., Zhu, X., Melnykov, V., and Ingrassia, S. (2020) On Parsimonious Models for Modeling Matrix Data, Computational Statistics and Data Analysis, 142, 1-26,

19. Melnykov, V. and Michael, S. (2020) Clustering Large Datasets by Merging K-Means Solutions, Journal of Classification, 37, 97-123.

20. Melnykov, V. and Zhu, X. (2019) An Extension of the K-Means Algorithm to Clustering Skewed Data, Computational Statistics, 34:1, 373-394.

21. Melnykov, V. and Zhu, X. (2019) Studying Crime Trends in the USA over the Years 2000-2012, Advances in Data Analysis and Classification, 13:1, 325-341.

22. Melnykov, V. and Zhu, X. (2018) On Model-Based Clustering of Skewed Matrix Data, Journal of Multivariate Analysis, 167, 181-194.

23. Zhu, X. and Melnykov, V. (2018) Manly Transformation in Finite Mixture Modeling, Computational Statistics and Data Analysis, 121, 190-208.

24. Zhu, X. and Melnykov, V. (2017) ManlyMix: An R Package for Manly Mixture Modeling, The R Journal, 9:2, 176-197.

25. Melnykov, Y., Melnykov, V. and Zhu, X. (2017) Studying Contributions of Variables to Classification, Statistics and Probability Letters, 129, 318-325.

26. Melnykov, V. (2016) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, Journal of Statistical Software, 74:9, 1-34.

27. Melnykov, V. (2016) Model-Based Biclustering of Clickstream Data, Computational Statistics and Data Analysis, 93, 31-45.

28. Melnykov, V. (2016) Merging Mixture Components for Clustering through Pairwise Overlap, Journal of Computational and Graphical Statistics, 25, 66-90.

29. Melnykov, V., Melnykov, I. and Michael, S. (2016) Semi-Supervised Model-Based Clustering with Positive and Negative Constraints, Advances in Data Analysis and Classification, 10:3, 327-349.

30. Michael, S. and Melnykov, V. (2016) Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology, Journal of Classification, 33:3, 412-441.

31. Michael, S. and Melnykov, V. (2016) An Effective Strategy for Initializing the EM algorithm in Finite Mixture Models, Advances in Data Analysis and Classification, 10:4, 563-583.

32. Michael, S. and Melnykov, V. (2016) Studying Complexity of Model-Based Clustering, Communications in Statistics – Simulation and Computation, 45:6, 2051-2069.

33. Zhu, X. and Melnykov, V. (2015) Probabilistic Assessment of Model-Based Clustering, Advances in Data Analysis and Classification, 9:4, 395-422.

34. Melnykov, V., Michael, S. and Melnykov, I. (2015) Recent Developments in Model-Based Clustering with Applications, Partitional Clustering Algorithms, ed. M. E. Celebi, Springer, 1-39.

35. Melnykov, I. and Melnykov, V. (2014) On K-Means Algorithm with the Use of Mahalanobis Distances, Statistics and Probability Letters, 84, 88-95.

36. Melnykov, V. (2013) On the Distribution of Posterior Probabilities in Finite Mixture Models with Application in Clustering, Journal of Multivariate Analysis, 122, 175-189.

37. Melnykov, V. (2013) Finite Mixture Modeling in Mass Spectrometry Analysis, Journal of the Royal Statistical Society: Series C, 62:4, 573-592.

38. Melnykov, V. (2013) Challenges in Model-Based Clustering, WIREs: Computational Statistics, 5:2, 135-148.

39. Melnykov, V. and Shen, G. (2013) Clustering through Empirical Likelihood Ratio, Computational Statistics and Data Analysis, 62, 1-10.

40. Melnykov, V., Chen, W.-C. and Maitra, R. (2012) MixSim: R Package for Simulating Datasets with Pre-Specified Clustering Complexity, Journal of Statistical Software, 51:12, 1-25.

41. Maitra, R., Melnykov, V. and Lahiri, S. (2012) Bootstrapping for Significance of Compact Clusters, Journal of the American Statistical Association, 107:497, 378-392.

42. Melnykov, V. and Melnykov, I. (2012) Initializing the EM Algorithm in Gaussian Mixture Models with an Unknown Number of Components, Computational Statistics and Data Analysis, 56:6, 1381-1395.

43. Melnykov, V. (2012) Efficient Estimation in Model-Based Clustering of Gaussian Regression Time Series, Statistical Analysis and Data Mining, 5:2, 95-99.

44. Melnykov, V., Maitra, R. and Nettleton, D. (2011) Accounting for Spot Matching Uncertainty in the Analysis of Proteomics Data from Two-Dimensional Gel Electrophoresis, Sankhya: Series B, 73:1, 123-143.

45. Melnykov, V. and Maitra, R. (2011) CARP: Software for Fishing Out Good Clustering Algorithms, Journal of Machine Learning Research, 12, 69-73.

46. Maitra, R. and Melnykov, V. (2010) Simulating Data to Study Performance of Finite Mixture Modeling and Clustering Algorithms, Journal of Computational and Graphical Statistics, 2:19, 354-376.

47. Melnykov, V. and Maitra, R. (2010) Finite Mixture Models and Model-Based Clustering, Statistics Surveys, 4, 80-116.