Nonparametric augmented probability weighting with sparsity
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DOI: 10.1016/j.csda.2023.107890
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- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018.
"Double/debiased machine learning for treatment and structural parameters,"
Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.
- Alberto Abadie & Guido W. Imbens, 2016.
"Matching on the Estimated Propensity Score,"
Econometrica, Econometric Society, vol. 84, pages 781-807, March.
- Alberto Abadie & Guido W. Imbens, 2009. "Matching on the Estimated Propensity Score," NBER Working Papers 15301, National Bureau of Economic Research, Inc.
- Peisong Han & Lu Wang, 2013. "Estimation with missing data: beyond double robustness," Biometrika, Biometrika Trust, vol. 100(2), pages 417-430.
- Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012.
"Inverse Probability Tilting for Moment Condition Models with Missing Data,"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
- Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2008. "Inverse Probability Tilting for Moment Condition Models with Missing Data," NBER Working Papers 13981, National Bureau of Economic Research, Inc.
- M.‐H. Chen & J. G. Ibrahim & C. Yiannoutsos, 1999. "Prior elicitation, variable selection and Bayesian computation for logistic regression models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 223-242.
- Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, November.
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- Wooldridge, Jeffrey M., 2007.
"Inverse probability weighted estimation for general missing data problems,"
Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
- Jeffrey M. Wooldridge, 2004. "Inverse probability weighted estimation for general missing data problems," CeMMAP working papers 05/04, Institute for Fiscal Studies.
- Jeffrey M. Wooldridge, 2004. "Inverse probability weighted estimation for general missing data problems," CeMMAP working papers CWP05/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Shu Yang & Jae Kwang Kim & Rui Song, 2020. "Doubly robust inference when combining probability and non‐probability samples with high dimensional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(2), pages 445-465, April.
- Yang Ning & Peng Sida & Kosuke Imai, 2020. "Robust estimation of causal effects via a high-dimensional covariate balancing propensity score," Biometrika, Biometrika Trust, vol. 107(3), pages 533-554.
- Xiangyu Wang & Chenlei Leng, 2016. "High dimensional ordinary least squares projection for screening variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 589-611, June.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
- Lin, Huazhen & Zhou, Fanyin & Wang, Qiuxia & Zhou, Ling & Qin, Jing, 2018. "Robust and efficient estimation for the treatment effect in causal inference and missing data problems," Journal of Econometrics, Elsevier, vol. 205(2), pages 363-380.
- Fan, Jianqing & Feng, Yang & Song, Rui, 2011. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 544-557.
- Kenji Fukumizu & Chenlei Leng, 2014. "Gradient-Based Kernel Dimension Reduction for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 359-370, March.
- Qu, Annie & Lindsay, Bruce G. & Lu, Lin, 2010. "Highly Efficient Aggregate Unbiased Estimating Functions Approach for Correlated Data With Missing at Random," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 194-204.
- Valerie C. Bradley & Shiro Kuriwaki & Michael Isakov & Dino Sejdinovic & Xiao-Li Meng & Seth Flaxman, 2021. "Unrepresentative big surveys significantly overestimated US vaccine uptake," Nature, Nature, vol. 600(7890), pages 695-700, December.
- Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
- Jing Qin & Biao Zhang & Denis H.Y. Leung, 2017. "Efficient Augmented Inverse Probability Weighted Estimation in Missing Data Problems," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 86-97, January.
- Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
- Xiaotong Shen & Wei Pan & Yunzhang Zhu & Hui Zhou, 2013. "On constrained and regularized high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 807-832, October.
- Wang, Hansheng, 2009. "Forward Regression for Ultra-High Dimensional Variable Screening," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1512-1524.
- Xiaotong Shen & Wei Pan & Yunzhang Zhu, 2012. "Likelihood-Based Selection and Sharp Parameter Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 223-232, March.
- Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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Keywords
Central limit theorem; Reproducing kernel Hilbert space; Nonresponse; Sparse learning;All these keywords.
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