References in zbMATH (referenced in 61 articles , 1 standard article )

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  1. Alqahtani, Nada A.; Kalantan, Zakiah I.: Gaussian mixture models based on principal components and applications (2020)
  2. Dychko, H. M.; Maĭboroda, R. E.: A generalized Nadaraya-Watson estimator for observations obtained from a mixture (2020)
  3. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  4. Lin, Zhixiang; Zamanighomi, Mahdi; Daley, Timothy; Ma, Shining; Wong, Wing Hung: Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression (2020)
  5. Murphy, Keefe; Murphy, Thomas Brendan: Gaussian parsimonious clustering models with covariates and a noise component (2020)
  6. Zheng, Chaowen; Wu, Yichao: Nonparametric estimation of multivariate mixtures (2020)
  7. Akakpo, Rexford M.; Xia, Michelle; Polansky, Alan M.: Frequentist inference in insurance ratemaking models adjusting for misrepresentation (2019)
  8. Blair R. Drummond, Christian J.G. Tessier, Mathieu F. Dextraze, Corrie J.B. daCosta: scbursts: An R package for analysis and sorting of single-channel bursts (2019) not zbMATH
  9. Comas-Cufí, Marc; Martín-Fernández, Josep A.; Mateu-Figueras, Glòria: Merging the components of a finite mixture using posterior probabilities (2019)
  10. Geissen, Eva-Maria; Hasenauer, Jan; Radde, Nicole E.: Inference of finite mixture models and the effect of binning (2019)
  11. Gualandi, Stefano; Toscani, Giuseppe: Human behavior and lognormal distribution. A kinetic description (2019)
  12. Lim, Johan; Yu, Donghyeon; Kuo, Hsun-Chih; Choi, Hyungwon; Walmsley, Scott: Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data (2019)
  13. Liu, Yiyi; Warren, Joshua L.; Zhao, Hongyu: A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data (2019)
  14. Maĭboroda, R. E.; Navara, G. V.; Sugakova, O. V.: Orthogonal regression method for observations from a mixture (2019)
  15. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  16. Nguyen, Hien; Yee, Yohan; McLachlan, Geoffrey; Lerch, Jason: False discovery rate control for grouped or discretely supported (p)-values with application to a neuroimaging study (2019)
  17. Śmieja, Marek; Hajto, Krzysztof; Tabor, Jacek: Efficient mixture model for clustering of sparse high dimensional binary data (2019)
  18. Young, Derek S.; Chen, Xi; Hewage, Dilrukshi C.; Nilo-Poyanco, Ricardo: Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (2019)
  19. Zagaris, Antonios: Data-informed modeling in the health sciences (2019)
  20. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH

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