1. Bechtel, Y. C., Bonaiti-Pellie, C., Poisson, N., Magnette, J., and Bechtel, P. R. (1993), A Population and Family Study N-Acetyltransferase Using Caffeine Urinary Metabolites. Clinical Pharmacology & Therapeutics, 54(2), 134-141. [ DOI:10.1038/clpt.1993.124] [ PMID] 2. Bouveyron, C., Celeux, G., Murphy, T. B., and Raftery, A. E. (2019), Model-Based Clustering and Classification for Data Science: with Applications in R. Cambridge University Press. 3. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the royal statistical society: series B (methodological), 39(1), 1-22. [ DOI:10.1111/j.2517-6161.1977.tb01600.x] 4. Dunn, J. C. (1974), Well-Separated Clusters and Optimal Fuzzy Partitions, Journal of Cybernetics, 4(1), 95-104. [ DOI:10.1080/01969727408546059] 5. Fraley, C., and Raftery, A. E. (2003), Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST. Journal of classification, 20(2), 263-286. [ DOI:10.1007/s00357-003-0015-3] 6. Fuller, W. A. (2009), Measurement Error Models, John Wiley & Sons. 7. Hubert, L., and Arabie, P. (1985), Comparing Partitions. Journal of classification, 2, 193-218. [ DOI:10.1007/BF01908075] 8. Komárek, A., and Komárková, L. (2014), Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data. Journal of Statistical Software, 59(12), 1-38. [ DOI:10.18637/jss.v059.i12] 9. Kong, A., McCullagh, P., Meng, X. L., Nicolae, D., and Tan, Z. (2009), A Theory of Statistical Models for Monte Carlo Integration. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65(3), 585-604. [ DOI:10.1111/1467-9868.00404] 10. Nolan, J. P. (2020), Stable Distributions: Models for Heavy-Tailed Data. Springer Cham. 11. Pankowska, P., and Oberski, D. L. (2020), The effect of Measurement Error on Clustering Algorithms. arXiv preprint arXiv, :2005.11743. 12. Ritter, G. (2015), Robust Cluster Analysis and Variable Selection, Vol. 137 of Chapman & Hall/CRC Monographs on Statistics & Applied Probability, CRC Press. 13. Rousseeuw, P. J. (1987), Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Journal of Computational and Applied Mathematics, 20, 53-65. [ DOI:10.1016/0377-0427(87)90125-7] 14. Salas-Gonzalez, D., Kuruoglu, E. E., and Ruiz, D. P. (2009), Finite Mixture of α-Stable Distributions. Digital Signal Processing , 250-264. [ DOI:10.1016/j.dsp.2007.11.004] 15. Samorodnitsky, G. and Taqqu, M. S. (1994), Stable non-Gaussian Random Processes, Chapman and Hall, New York. 16. Schwarz, G. (1978), Estimating the Dimension of a Model. The annals of statistics, 461-464. 17. Teimouri, M. (2020). Maximum Likelihood Estimator of the α-Stable Distribution, Journal of Statistical Sciences, 14, 73-94. [ DOI:10.29252/jss.14.1.75] 18. Scrucca, L., Fop, M., Murphy, T. B., and Raftery, A. E. (2016), mclust 5: Mlustering, Classification and Ddensity Estimation using Gaussian Finite Mixture Models. Journal of the R, 8(1), 205-233. [ DOI:10.32614/RJ-2016-021] [ PMID] [ ] 19. Zarei, S. (2021). Robust Empirical Bayes Small Area Estimation with Symmetric α-Stable Distribution for Error Components, Journal of Statistical Sciences, 15(2), 463-480. [ DOI:10.52547/jss.15.2.463] 20. Zarei, S., and Mohammdpour, A. (2020), Pseudo-Stochastic EM for sub-Gaussian α-Stable Mixture Models. Digital Signal Processing. doi.org/10.1016/j.dsp.2020.102671. 99 102671. [ DOI:10.1016/j.dsp.2020.102671] 21. Zhang, W., and Di, Y. (2020), Model-Based Clustering with Measurement or Estimation Errors, Genes, 11(2), 185-209. [ DOI:10.3390/genes11020185] [ PMID] [ ]
|