Information Systems, Statistics and Management Science
Computational Statistics, Data Mining, Finite Mixture Models, Cluster Analysis, and Statistical Graphics
Bowling Green State University (M.S. in Statistics, 2005), Iowa State University (Ph.D in Statistics, 2009)
Dr. Melnykov joined the Applied Statistics faculty in 2012. His research has been published in the Journal of the American Statistical Association, Journal of the Royal Statistical Society, Journal of Computational and Graphical Statistics, Computational Statistics and Data Analysis, Journal of Machine Learning Research, and others. He is a member of the American Statistical Association.
Melnykov, V. (2013) Challenges in Model-Based Clustering, accepted by WIREs: Computational Statistics.
Melnykov, V. (2013) Finite Mixture Modeling in Mass Spectrometry Analysis, accepted by Journal of the Royal Statistical Society: Series C.
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.
Maitra, R., Melnykov, V. and Lahiri, S. (2012) Bootstrapping for Significance of Compact Clusters, Journal of the American Statistical Association, 107:497, 378-392.
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.
Melnykov, V. (2012) Efficient Estimation in Model-Based Clustering of Gaussian Regression Time Series, Statistical Analysis and Data Mining, 5:2, 95-99.
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.
Melnykov, V. and Maitra, R. (2011) CARP: Software for Fishing Out Good Clustering Algorithms, Journal of Machine Learning Research, 12, 69-73.
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.
11. Melnykov, V. and Maitra, R. (2010) Finite Mixture Models and Model-Based Clustering, Statistics Surveys, 4, 80-116.
1. Melnykov, V. and Shen, G. (2013) Clustering through Empirical Likelihood Ratio, accepted by Computational Statistics and Data Analysis.