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Variable Selection in Finite Mixture of Regression Models

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Cited by:

  1. Monia Ranalli & Roberto Rocci, 2024. "Composite likelihood methods for parsimonious model-based clustering of mixed-type data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 381-407, June.
  2. Saverio Ranciati & Giuliano Galimberti & Gabriele Soffritti, 2019. "Bayesian variable selection in linear regression models with non-normal errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 323-358, June.
  3. Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.
  4. Na You & Shun He & Xueqin Wang & Junxian Zhu & Heping Zhang, 2018. "Subtype classification and heterogeneous prognosis model construction in precision medicine," Biometrics, The International Biometric Society, vol. 74(3), pages 814-822, September.
  5. Pei, Youquan & Peng, Heng & Xu, Jinfeng, 2024. "A latent class Cox model for heterogeneous time-to-event data," Journal of Econometrics, Elsevier, vol. 239(2).
  6. Djeutem, Edouard & Dunbar, Geoffrey R., 2022. "Uncovered return parity: Equity returns and currency returns," Journal of International Money and Finance, Elsevier, vol. 128(C).
  7. Semhar Michael & Volodymyr Melnykov, 2016. "Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 412-441, October.
  8. Gerhard Tutz & Margret-Ruth Oelker, 2017. "Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures," International Statistical Review, International Statistical Institute, vol. 85(2), pages 204-227, August.
  9. 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).
  10. Monbet, Valérie & Ailliot, Pierre, 2017. "Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 40-51.
  11. Rosaria Simone, 2021. "An accelerated EM algorithm for mixture models with uncertainty for rating data," Computational Statistics, Springer, vol. 36(1), pages 691-714, March.
  12. 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.
  13. Gustavo Alexis Sabillón & Luiz Gabriel Fernandes Cotrim & Daiane Aparecida Zuanetti, 2023. "A data-driven reversible jump for estimating a finite mixture of regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 350-369, March.
  14. 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.
  15. Dalei Yu, 2016. "Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1214-1235, December.
  16. Ahonen, Ilmari & Nevalainen, Jaakko & Larocque, Denis, 2019. "Prediction with a flexible finite mixture-of-regressions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 212-224.
  17. Shao, Lihui & Wu, Jiaqi & Zhang, Weiping & Chen, Yu, 2024. "Integrated subgroup identification from multi-source data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
  18. Yu, Jing & Nummi, Tapio & Pan, Jianxin, 2022. "Mixture regression for longitudinal data based on joint mean–covariance model," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  19. Dunstan, Piers K. & Foster, Scott D. & Darnell, Ross, 2011. "Model based grouping of species across environmental gradients," Ecological Modelling, Elsevier, vol. 222(4), pages 955-963.
  20. Devijver, Emilie, 2017. "Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 1-13.
  21. 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).
  22. Jennifer S. K. Chan & S. T. Boris Choy & Udi Makov & Ariel Shamir & Vered Shapovalov, 2022. "Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data," Risks, MDPI, vol. 10(4), pages 1-10, April.
  23. Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.
  24. Galimberti, Giuliano & Montanari, Angela & Viroli, Cinzia, 2009. "Penalized factor mixture analysis for variable selection in clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4301-4310, October.
  25. Ciarleglio, Adam & Todd Ogden, R., 2016. "Wavelet-based scalar-on-function finite mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 86-96.
  26. Abbas Khalili & Shili Lin, 2013. "Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters," Biometrics, The International Biometric Society, vol. 69(2), pages 436-446, June.
  27. Chao Huang & Martin Styner & Hongtu Zhu, 2015. "Clustering High-Dimensional Landmark-Based Two-Dimensional Shape Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 946-961, September.
  28. Da Silva Catela, Eva Yamila & Gonçalves, Flávio & Porcile, Gabriel, 2010. "Brazilian municipalities: agglomeration economies and development levels in 1997 and 2007," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
  29. Marco Berrettini & Giuliano Galimberti & Saverio Ranciati, 2023. "Semiparametric finite mixture of regression models with Bayesian P-splines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 745-775, September.
  30. Dong, Wei & Xu, Chen & Xie, Jinhan & Tang, Niansheng, 2024. "Tuning-free sparse clustering via alternating hard-thresholding," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
  31. Tang, Qingguo & Karunamuni, Rohana J., 2013. "Minimum distance estimation in a finite mixture regression model," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 185-204.
  32. 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.
  33. Nicolas Städler & Peter Bühlmann & Sara Geer, 2010. "Rejoinder: ℓ 1 -penalization for mixture regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(2), pages 280-285, August.
  34. Stéphane Chrétien & Alfred Hero & Hervé Perdry, 2012. "Space alternating penalized Kullback proximal point algorithms for maximizing likelihood with nondifferentiable penalty," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(4), pages 791-809, August.
  35. Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 45-61, March.
  36. Lee, Kuo-Jung & Chen, Ray-Bing & Wu, Ying Nian, 2016. "Bayesian variable selection for finite mixture model of linear regressions," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 1-16.
  37. Ping Zeng & Yongyue Wei & Yang Zhao & Jin Liu & Liya Liu & Ruyang Zhang & Jianwei Gou & Shuiping Huang & Feng Chen, 2014. "Variable selection approach for zero-inflated count data via adaptive lasso," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 879-894, April.
  38. Baihua He & Tingyan Zhong & Jian Huang & Yanyan Liu & Qingzhao Zhang & Shuangge Ma, 2021. "Histopathological imaging‐based cancer heterogeneity analysis via penalized fusion with model averaging," Biometrics, The International Biometric Society, vol. 77(4), pages 1397-1408, December.
  39. Abbas Khalili & Farhad Shokoohi & Masoud Asgharian & Shili Lin, 2023. "Sparse estimation in semiparametric finite mixture of varying coefficient regression models," Biometrics, The International Biometric Society, vol. 79(4), pages 3445-3457, December.
  40. Mingyang Ren & Qingzhao Zhang & Sanguo Zhang & Tingyan Zhong & Jian Huang & Shuangge Ma, 2022. "Hierarchical cancer heterogeneity analysis based on histopathological imaging features," Biometrics, The International Biometric Society, vol. 78(4), pages 1579-1591, December.
  41. Nicolas Städler & Peter Bühlmann & Sara Geer, 2010. "ℓ 1 -penalization for mixture regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(2), pages 209-256, August.
  42. Dannemann, Jörn & Holzmann, Hajo, 2010. "Testing for two components in a switching regression model," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1592-1604, June.
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