- Zhu, X. and Melnykov, V. (2016) Manly Transformation in Finite Mixture Modeling, accepted by Computational Statistics and Data Analysis.
- Porter, M.D. (2016) “A Statistical Approach to Crime Linkage”, accepted by The American Statistician, (arXiv:1410.2285)
- 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.
- 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.
- Bouhana, N., Johnson, S.D., and Porter, M.D. “Consistency and speciﬁcity in burglars who commit prolific residential burglary: Testing the core assumptions underpinning behavioural crime linkage” Legal and Criminological Psychology (Accepted)
- 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.
- Porter, M.D. and Brown, D.E. (2007). “Detecting Local Regions of Change in High-Dimensional Criminal or Terrorist Point Processes”. Computational Statistics and Data Analysis, 51, 2753-2768