Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters
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Cited by:
- Lloyd-Jones, Luke R. & Nguyen, Hien D. & McLachlan, Geoffrey J., 2018. "A globally convergent algorithm for lasso-penalized mixture of linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 19-38.
- Lee, Kuo-Jung & Feldkircher, Martin & Chen, Yi-Chi, 2021. "Variable selection in finite mixture of regression models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
- Liu, Mengque & Zhang, Qingzhao & Fang, Kuangnan & Ma, Shuangge, 2020. "Structured analysis of the high-dimensional FMR model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
- Qingguo Tang & R. J. Karunamuni, 2018. "Robust variable selection for finite mixture regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 489-521, June.
- Qin, Xing & Hu, Jianhua & Ma, Shuangge & Wu, Mengyun, 2024. "Estimation of multiple networks with common structures in heterogeneous subgroups," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
- Yan Li & Chun Yu & Yize Zhao & Weixin Yao & Robert H. Aseltine & Kun Chen, 2022. "Pursuing sources of heterogeneity in modeling clustered population," Biometrics, The International Biometric Society, vol. 78(2), pages 716-729, June.
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