Estimation and Accuracy After Model Selection

Citations

Cited by:

1. John Copas & Shinto Eguchi, 2020. "Strong model dependence in statistical analysis: goodness of fit is not enough for model choice," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 329-352, April.
2. Dean Dustin & Bertrand Clarke, 2024. "Post-Model-Selection Prediction Intervals for Generalized Linear Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 301-326, November.
3. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
4. Gourieroux, Christian & Jasiak, Joann, 2010. "Inference for Noisy Long Run Component Process," MPRA Paper 98987, University Library of Munich, Germany.
5. Long Mark C. & Rooklyn Jordan, 2024. "Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-21, January.
6. Lai Xinglin, 2021. "Modelling hetegeneous treatment effects by quantitle local polynomial decision tree and forest," Papers 2111.15320, arXiv.org, revised Mar 2022.
7. Jimmy Semakula & Rene A. Corner-Thomas & Stephen T. Morris & Hugh T. Blair & Paul R. Kenyon, 2021. "The Effect of Herbage Availability and Season of Year on the Rate of Liveweight Loss during Weighing of Fasting Ewe Lambs," Agriculture, MDPI, vol. 11(2), pages 1-20, February.
8. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
9. Merlin Stein, 2022. "When are large female-led firms more resilient against shocks? Learnings from Indian enterprises during COVID-19 with diff-in-diff and causal forests," CSAE Working Paper Series 2022-01, Centre for the Study of African Economies, University of Oxford.
10. Subhadeep & Mukhopadhyay, 2022. "Modelplasticity and Abductive Decision Making," Papers 2203.03040, arXiv.org, revised Mar 2023.
11. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
12. Maur,Jean-Christophe & Nedeljkovic,Milan & Von Uexkull,Jan Erik, 2022. "FDI and Trade Outcomes at the Industry Level—A Data-Driven Approach," Policy Research Working Paper Series 9901, The World Bank.
13. Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for the Akaike information criterion," Biometrika, Biometrika Trust, vol. 105(3), pages 645-664.
14. Wu Wang & Xuming He & Zhongyi Zhu, 2020. "Statistical inference for multiple change‐point models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1149-1170, December.
15. Pan, Jia-Chiun & Huang, Yufen & Hwang, J.T. Gene, 2017. "Estimation of selected parameters," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 45-63.
16. D. J. Eck & R. D. Cook, 2017. "Weighted envelope estimation to handle variability in model selection," Biometrika, Biometrika Trust, vol. 104(3), pages 743-749.
17. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    • Athey, Susan & Tibshirani, Julie & Wager, Stefan, 2017. "Generalized Random Forests," Research Papers 3575, Stanford University, Graduate School of Business.
18. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    • Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
19. Fang Fang & Jiwei Zhao & S. Ejaz Ahmed & Annie Qu, 2021. "A weak‐signal‐assisted procedure for variable selection and statistical inference with an informative subsample," Biometrics, The International Biometric Society, vol. 77(3), pages 996-1010, September.
20. Subhadeep Mukhopadhyay, 2023. "Modelplasticity and abductive decision making," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 255-276, June.
21. Arlen Dean & Amirhossein Meisami & Henry Lam & Mark P. Van Oyen & Christopher Stromblad & Nick Kastango, 2022. "Quantile regression forests for individualized surgery scheduling," Health Care Management Science, Springer, vol. 25(4), pages 682-709, December.
22. Mody, Ashoka & Nedeljkovic, Milan, 2024. "Central bank policies and financial markets: Lessons from the euro crisis," Journal of Banking & Finance, Elsevier, vol. 158(C).
23. Ruth M. Pfeiffer & Andrew Redd & Raymond J. Carroll, 2017. "On the impact of model selection on predictor identification and parameter inference," Computational Statistics, Springer, vol. 32(2), pages 667-690, June.
24. Yongli Zhang & Xiaotong Shen, 2015. "Adaptive Modeling Procedure Selection by Data Perturbation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 541-551, October.
25. Yiqi Liu & Yuan Qi, 2023. "Using Forests in Multivariate Regression Discontinuity Designs," Papers 2303.11721, arXiv.org, revised Jul 2024.
26. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
27. Nigel Stallard & Peter K Kimani, 2018. "Uniformly minimum variance conditionally unbiased estimation in multi-arm multi-stage clinical trials," Biometrika, Biometrika Trust, vol. 105(2), pages 495-501.
28. Christian Hennig & Willi Sauerbrei, 2019. "Exploration of the variability of variable selection based on distances between bootstrap sample results," 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. 13(4), pages 933-963, December.
29. Daniel Jacob, 2021. "Variable Selection for Causal Inference via Outcome-Adaptive Random Forest," Papers 2109.04154, arXiv.org.
30. Lenard Lieb & Stephan Smeekes, 2017. "Inference for Impulse Responses under Model Uncertainty," Papers 1709.09583, arXiv.org, revised Oct 2019.
    • Lieb, Lenard & Smeekes, Stephan, 2017. "Inference for Impulse Responses under Model Uncertainty," Research Memorandum 022, Maastricht University, Graduate School of Business and Economics (GSBE).
31. David J. Olive, 2018. "Applications of hyperellipsoidal prediction regions," Statistical Papers, Springer, vol. 59(3), pages 913-931, September.
32. Céline Cunen & Nils Lid Hjort, 2020. "Confidence Distributions for FIC Scores," Econometrics, MDPI, vol. 8(3), pages 1-28, July.
33. He Kevin & Zhou Xiang & Jiang Hui & Wen Xiaoquan & Li Yi, 2018. "False discovery control for penalized variable selections with high-dimensional covariates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(6), pages 1-11, December.
34. Wu, Suofei & Hannig, Jan & Lee, Thomas C.M., 2022. "Uncertainty quantification for honest regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
35. Lasanthi C. R. Pelawa Watagoda & David J. Olive, 2021. "Bootstrapping multiple linear regression after variable selection," Statistical Papers, Springer, vol. 62(2), pages 681-700, April.