IDEAS home Printed from https://ideas.repec.org/r/bla/jorssb/v71y2009i5p1009-1030.html
   My bibliography  Save this item

Sparse additive models

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Du, Pang & Cheng, Guang & Liang, Hua, 2012. "Semiparametric regression models with additive nonparametric components and high dimensional parametric components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2006-2017.
  2. Goessling, Marc, 2017. "LogitBoost autoregressive networks," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 88-98.
  3. Giordano, Francesco & Parrella, Maria Lucia, 2016. "Bias-corrected inference for multivariate nonparametric regression: Model selection and oracle property," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 71-93.
  4. Philipp Bach & Sven Klaassen & Jannis Kueck & Martin Spindler, 2020. "Estimation and Uniform Inference in Sparse High-Dimensional Additive Models," Papers 2004.01623, arXiv.org, revised Apr 2024.
  5. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
  6. Yoshida, Takuma & Naito, Kanta, 2019. "Regression with stagewise minimization on risk function," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 123-143.
  7. Kuang-Yao Lee & Bing Li & Hongyu Zhao, 2016. "Variable selection via additive conditional independence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1037-1055, November.
  8. Zhu, Ying, 2015. "Sparse Linear Models and l1−Regularized 2SLS with High-Dimensional Endogenous Regressors and Instruments," MPRA Paper 81217, University Library of Munich, Germany.
  9. Xia Zheng & Yaohua Rong & Ling Liu & Weihu Cheng, 2021. "A More Accurate Estimation of Semiparametric Logistic Regression," Mathematics, MDPI, vol. 9(19), pages 1-12, September.
  10. Zaili Fang & Inyoung Kim & Patrick Schaumont, 2016. "Flexible variable selection for recovering sparsity in nonadditive nonparametric models," Biometrics, The International Biometric Society, vol. 72(4), pages 1155-1163, December.
  11. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  12. Nardi, Y. & Rinaldo, A., 2011. "Autoregressive process modeling via the Lasso procedure," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 528-549, March.
  13. Yuping Zhang & Zhengqing Ouyang, 2018. "Joint principal trend analysis for longitudinal high†dimensional data," Biometrics, The International Biometric Society, vol. 74(2), pages 430-438, June.
  14. Hang Yu & Yuanjia Wang & Donglin Zeng, 2023. "A general framework of nonparametric feature selection in high‐dimensional data," Biometrics, The International Biometric Society, vol. 79(2), pages 951-963, June.
  15. Virginia X. He & Matt P. Wand, 2024. "Bayesian generalized additive model selection including a fast variational option," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 639-668, September.
  16. Luu, Tung Duy & Fadili, Jalal & Chesneau, Christophe, 2019. "PAC-Bayesian risk bounds for group-analysis sparse regression by exponential weighting," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 209-233.
  17. Shizhe Chen & Ali Shojaie & Daniela M. Witten, 2017. "Network Reconstruction From High-Dimensional Ordinary Differential Equations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1697-1707, October.
  18. Jiang, Liewen & Bondell, Howard D. & Wang, Huixia Judy, 2014. "Interquantile shrinkage and variable selection in quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 208-219.
  19. Shaogao Lv & Xin He & Junhui Wang, 2017. "A unified penalized method for sparse additive quantile models: an RKHS approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 897-923, August.
  20. Umberto Amato & Anestis Antoniadis & Italia De Feis, 2016. "Additive model selection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 519-564, November.
  21. Yen, Tso-Jung & Yen, Yu-Min, 2016. "Structured variable selection via prior-induced hierarchical penalty functions," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 87-103.
  22. Hyung Park & Thaddeus Tarpey & Eva Petkova & R. Todd Ogden, 2024. "A high-dimensional single-index regression for interactions between treatment and covariates," Statistical Papers, Springer, vol. 65(7), pages 4025-4056, September.
  23. He, Yong & Zhang, Xinsheng & Zhang, Liwen, 2018. "Variable selection for high dimensional Gaussian copula regression model: An adaptive hypothesis testing procedure," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 132-150.
  24. Yang Ni & Francesco C. Stingo & Veerabhadran Baladandayuthapani, 2015. "Bayesian nonlinear model selection for gene regulatory networks," Biometrics, The International Biometric Society, vol. 71(3), pages 585-595, September.
  25. Xia Cui & Heng Peng & Songqiao Wen & Lixing Zhu, 2013. "Component Selection in the Additive Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 491-510, September.
  26. Fu, Penghui & Tan, Zhiqiang, 2024. "Block-wise primal-dual algorithms for large-scale doubly penalized ANOVA modeling," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
  27. Siddhartha Nandy & Chae Young Lim & Tapabrata Maiti, 2017. "Additive model building for spatial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 779-800, June.
  28. Radchenko, Peter, 2015. "High dimensional single index models," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 266-282.
  29. Bhatnagar, Sahir R. & Lu, Tianyuan & Lovato, Amanda & Olds, David L. & Kobor, Michael S. & Meaney, Michael J. & O'Donnell, Kieran & Yang, Archer Y. & Greenwood, Celia M.T., 2023. "A sparse additive model for high-dimensional interactions with an exposure variable," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  30. Bergersen, Linn Cecilie & Tharmaratnam, Kukatharmini & Glad, Ingrid K., 2014. "Monotone splines lasso," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 336-351.
  31. Sayanti Guha Majumdar & Anil Rai & Dwijesh Chandra Mishra, 2023. "Estimation of Error Variance in Genomic Selection for Ultrahigh Dimensional Data," Agriculture, MDPI, vol. 13(4), pages 1-16, April.
  32. Fabian Scheipl & Thomas Kneib & Ludwig Fahrmeir, 2013. "Penalized likelihood and Bayesian function selection in regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 349-385, October.
  33. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
  34. Tao, Yanfang & Song, Biqin & Li, Luoqing, 2018. "Error analysis for coefficient-based regularized regression in additive models," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 22-28.
  35. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  36. Zhu, Ying, 2013. "Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors," MPRA Paper 49846, University Library of Munich, Germany.
  37. Yoshida, Takuma, 2018. "Semiparametric method for model structure discovery in additive regression models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 124-136.
  38. Ma, Haiqiang & Zhu, Zhongyi, 2016. "Continuously dynamic additive models for functional data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 1-13.
  39. Song Song, 2011. "Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach," Papers 1106.3921, arXiv.org, revised Jun 2011.
  40. McKay Curtis, S. & Banerjee, Sayantan & Ghosal, Subhashis, 2014. "Fast Bayesian model assessment for nonparametric additive regression," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 347-358.
  41. Gen Li & Sungkyu Jung, 2017. "Incorporating covariates into integrated factor analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 73(4), pages 1433-1442, December.
  42. Lin, Hongmei & Lian, Heng & Liang, Hua, 2019. "Rank reduction for high-dimensional generalized additive models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 672-684.
  43. Hanzhang Qin & David Simchi-Levi & Li Wang, 2022. "Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models," Management Science, INFORMS, vol. 68(9), pages 6591-6609, September.
  44. Morteza Amini & Mahdi Roozbeh, 2019. "Improving the prediction performance of the LASSO by subtracting the additive structural noises," Computational Statistics, Springer, vol. 34(1), pages 415-432, March.
  45. Takuma Yoshida, 2019. "Two stage smoothing in additive models with missing covariates," Statistical Papers, Springer, vol. 60(6), pages 1803-1826, December.
  46. Fan, Jianqing & Feng, Yang & Xia, Lucy, 2020. "A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models," Journal of Econometrics, Elsevier, vol. 218(1), pages 119-139.
  47. Kyle R. White & Leonard A. Stefanski & Yichao Wu, 2017. "Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1587-1597, October.
  48. Randy C. S. Lai & Jan Hannig & Thomas C. M. Lee, 2015. "Generalized Fiducial Inference for Ultrahigh-Dimensional Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 760-772, June.
  49. Kuang‐Yao Lee & Lexin Li, 2022. "Functional structural equation model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 600-629, April.
  50. Ding, Hui & Zhang, Jian & Zhang, Riquan, 2022. "Nonparametric variable screening for multivariate additive models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
  51. Hyung Park & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Functional additive models for optimizing individualized treatment rules," Biometrics, The International Biometric Society, vol. 79(1), pages 113-126, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.