Source: http://sla.ua.edu/publications.html
Timestamp: 2019-04-24 15:54:29+00:00

Document:
Melnykov, Y., Melnykov, V. and Zhu, X. (2017) Studying Contributions of Variables to Classification, accepted by Statistics and Probability Letters.
Melnykov, V. (2016) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, Journal of Statistical Software, 74:9, 1-34.
Melnykov, V. (2016) Merging Mixture Components for Clustering through Pairwise Overlap, Journal of Computational and Graphical Statistics, 25, 66-90.
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.
Michael, S. and Melnykov, V. (2016) Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology, accepted by Journal of Classification.
Porter, M.D. (2016) “A Statistical Approach to Crime Linkage”, The American Statistician, 70(2), 152-165.
Michael, S. and Melnykov, V. (2016) An Effective Strategy for Initializing the EM algorithm in Finite Mixture Models, accepted by Advances in Data Analysis and Classification.
Michael, S. and Melnykov, V. (2016) Studying Complexity of Model-Based Clustering, Communications in Statistics - Simulation and Computation, 45:6, 2051-2069.
Zhu, X. and Melnykov, V. (2016) Manly Transformation in Finite Mixture Modeling, accepted by Computational Statistics and Data Analysis.
Melnykov, V. (2016) Model-Based Biclustering of Clickstream Data, Computational Statistics and Data Analysis, 93, 31-45.
Wang, K., Simandl, J.K, Porter, M.D., Graettinger, A.J., Smith, R.K. (2016) “How the Choice of Safety Performance Function Affects the Identification of Important Crash Prediction Variables” Accident Analysis & Prevention, 88(1), 1-8.
Bouhana, N., Johnson, S.D., and Porter, M.D. (2016) “Consistency and speciﬁcity in burglars who commit prolific residential burglary: Testing the core assumptions underpinning behavioural crime linkage” Legal and Criminological Psychology, 21(1), 77-94.
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.
Reich, B.J. and Porter, M.D. (2015) “Partially-supervised spatiotemporal clustering for burglary crime series identification” Journal of the Royal Statistical Society-A 178(2), 465-480.
Melnykov, V. (2015) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, accepted by Journal of Statistical Software.
Melnykov, V., Melnykov, I. and Michael, S. (2015) Semi-Supervised Model-Based Clustering with Positive and Negative Constraints, accepted by Advances in Data Analysis and Classification.
Zhu, X. and Melnykov, V. (2015) Probabilistic Assessment of Model-Based Clustering, accepted by Advances in Data Analysis and Classification, 9:4, 395-422.
Melnykov, V. (2014) Merging Mixture Components for Clustering through Pairwise Overlap, accepted by Journal of Computational and Graphical Statistics.
Michael, S. and Melnykov, V. (2014) Studying Complexity of Model-Based Clustering, accepted by Communications in Statistics - Simulation and Computation.
White, G., Mazerolle, L., Porter, M.D., and Chalk, P. (2014) “Modelling the effectiveness of counter-terrorism interventions” Trends & Issues in Crime and Criminal Justice, (No. 475), 1-8.
White, G. and Porter, M.D. (2014) “GPU Accelerated MCMC for Modelling Terrorist Activity” Computational Statistics and Data Analysis, 71, 643-651.
Melnykov, I. and Melnykov, V. (2014) On K-Means Algorithm with the Use of Mahalanobis Distances, Statistics and Probability Letters, 84, 88-95.
Melnykov, V. (2013) On the Distribution of Posterior Probabilities in Finite Mixture Models with Application in Clustering, Journal of Multivariate Analysis, 122, 175-189.
Reich, B.J. and Porter, M.D. (2013) “Discussion of Clauset and Woodard: Estimating the Historical and Future Probabilities of Large Terrorist Events” Annals of Applied Statistics, 7(4), 1871-1875.
Melnykov, V. (2013) Finite Mixture Modeling in Mass Spectrometry Analysis, Journal of the Royal Statistical Society: Series C, 62:4, 573-592.
White, G., Porter, M.D., and Mazerolle, L. (2013) “Terrorism Risk, Resilience, and Volatility: A Comparison of Terrorism in Three Southeast Asian Countries”. Journal of Quantitative Criminology, 29(2), 295-320.
Melnykov, V. (2013) Challenges in Model-Based Clustering, WIREs: Computational Statistics, 5:2, 135-148.
Melnykov, V. and Shen, G. (2013) Clustering through Empirical Likelihood Ratio, Computational Statistics and Data Analysis, 62, 1-10.
Porter, M.D. and White, G. (2012) "Self-Exciting Hurdle Models for Terrorist Activity". Annals of Applied Statistics, 6(1), 106-124.
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.
Porter, M.D., White, G., and Mazerolle, L. (2012). “Innovative Methods for Terrorism and Counterterrorism Data”. In Evidence-Based Counterterrorism Policy, Lum, C. and Kennedy, L. (eds.), New York, NY: Springer.
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.
Porter, M.D. and Reich, B.J. (2012) "Evaluating temporally weighted kernel density methods for predicting the next event location in a series". Annals of GIS, 18(3), 225-240.
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.
Porter, M.D. and Smith, R. (2010). “Network Neighborhood Analysis” in IEEE Int. Conf. on Intelligence and Security Informatics (ISI), Vancouver, BC, Canada, pp. 31-36.
Melnykov, V. and Maitra, R. (2010) Finite Mixture Models and Model-Based Clustering, Statistics Surveys, 4, 80-116.
Porter, M.D., Neimi, J.B., and Reich, B.J. (2008). “Mixture Likelihood Ratio Scan Statistic for Disease Surveillance”. Advances in Disease Surveillance, Vol.5, No.1, pp. 49.

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