The Clustering of Regression Models Method with Applications in Gene Expression Data
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- Coffey, N. & Hinde, J. & Holian, E., 2014. "Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 14-29.
- Cremaschini, Alessandro & Maruotti, Antonello, 2023. "A finite mixture analysis of structural breaks in the G-7 gross domestic product series," Research in Economics, Elsevier, vol. 77(1), pages 76-90.
- Geoffrey Coke & Min Tsao, 2010. "Random effects mixture models for clustering electrical load series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 451-464, November.
- Tsai, Guei-Feng & Qu, Annie, 2008. "Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1387-1398, January.
- Gabriele Perrone & Gabriele Soffritti, 2023. "Seemingly unrelated clusterwise linear regression for contaminated data," Statistical Papers, Springer, vol. 64(3), pages 883-921, June.
- Angelo Mazza & Antonio Punzo, 2020. "Mixtures of multivariate contaminated normal regression models," Statistical Papers, Springer, vol. 61(2), pages 787-822, April.
- Han, Shengtong & Zhang, Hongmei & Karmaus, Wilfried & Roberts, Graham & Arshad, Hasan, 2017. "Adjusting background noise in cluster analyses of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 93-104.
- Zhang, Tonglin & Lin, Ge, 2021. "Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
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