Oracle inequalities for multi-fold cross validation
Author
Abstract
Suggested Citation
DOI: 10.1524/stnd.2006.24.3.351
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Andrews, Donald W. K., 1991. "Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 359-377, February.
- Laan Mark J. van der & Dudoit Sandrine & Vaart Aad W. van der, 2006. "The cross-validated adaptive epsilon-net estimator," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 1-23, December.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
- Zihao Li & Hui Lan & Vasilis Syrgkanis & Mengdi Wang & Masatoshi Uehara, 2024. "Regularized DeepIV with Model Selection," Papers 2403.04236, arXiv.org.
- van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
- Díaz Muñoz Iván & van der Laan Mark J., 2011. "Super Learner Based Conditional Density Estimation with Application to Marginal Structural Models," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-20, October.
- I Díaz & O Savenkov & K Ballman, 2018. "Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes," Biometrika, Biometrika Trust, vol. 105(3), pages 723-738.
- Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
- Fahimeh Hadavimoghaddam & Mehdi Ostadhassan & Ehsan Heidaryan & Mohammad Ali Sadri & Inna Chapanova & Evgeny Popov & Alexey Cheremisin & Saeed Rafieepour, 2021. "Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations," Energies, MDPI, vol. 14(4), pages 1-16, February.
- Mahmood Zafar & Khan Salahuddin, 2009. "On the Use of K-Fold Cross-Validation to Choose Cutoff Values and Assess the Performance of Predictive Models in Stepwise Regression," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-21, July.
- van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
- Zhang, Yongli & Yang, Yuhong, 2015. "Cross-validation for selecting a model selection procedure," Journal of Econometrics, Elsevier, vol. 187(1), pages 95-112.
- Laan Mark J. van der & Dudoit Sandrine & Vaart Aad W. van der, 2006. "The cross-validated adaptive epsilon-net estimator," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 1-23, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Broadie, Mark & Detemple, Jerome & Ghysels, Eric & Torres, Olivier, 2000.
"Nonparametric estimation of American options' exercise boundaries and call prices,"
Journal of Economic Dynamics and Control, Elsevier, vol. 24(11-12), pages 1829-1857, October.
- Mark Broadie & Jérôme Detemple & Eric Ghysels & Olivier Torrès, 1996. "Nonparametric Estimation of American Options Exercise Boundaries and Call Prices," CIRANO Working Papers 96s-24, CIRANO.
- Delgado, Miguel A & Robinson, Peter M, 1992.
"Nonparametric and Semiparametric Methods for Economic Research,"
Journal of Economic Surveys, Wiley Blackwell, vol. 6(3), pages 201-249.
- Delgado, Miguel A. & Robinson, Peter M., 1992. "Nonparametric and semiparametric methods for economic research," UC3M Working papers. Economics 2827, Universidad Carlos III de Madrid. Departamento de EconomÃa.
- Frank A. Schmid, 2003. "Conjectural guarantees loom large: evidence from the stock returns of Fannie Mae and Freddie Mac," Working Papers 2003-031, Federal Reserve Bank of St. Louis.
- Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021.
"Time-varying model averaging,"
Journal of Econometrics, Elsevier, vol. 222(2), pages 974-992.
- Yongmiao Hong & Tae-Hwy Lee & Yuying Sun & Shouyang Wang & Xinyu Zhang, 2017. "Time-varying Model Averaging," Working Papers 202001, University of California at Riverside, Department of Economics.
- Sainan Jin & Liangjun Su & Yonghui Zhang, 2015.
"Nonparametric testing for anomaly effects in empirical asset pricing models,"
Empirical Economics, Springer, vol. 48(1), pages 9-36, February.
- Sainan Jin & Liangjun Su & Yonghui Zhang, 2014. "Nonparametric Testing for Anomaly Effects in Empirical Asset Pricing Models," Working Papers 09-2014, Singapore Management University, School of Economics.
- Cong Li & Qi Li & Jeffrey Racine & DAIQIANG ZHANG, 2017. "Optimal Model Averaging Of Varying Coefficient Models," Department of Economics Working Papers 2017-01, McMaster University.
- Goldsmith, Jeff & Scheipl, Fabian, 2014. "Estimator selection and combination in scalar-on-function regression," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 362-372.
- Harley Frazis & Jay Stewart, 2006.
"How Does Household Production Affect Earnings Inequality?: Evidence from the American Time Use Survey,"
Economics Working Paper Archive
wp_454, Levy Economics Institute.
- Harley Frazis & Jay Stewart, 2006. "How Does Household Production Affect Earnings Inequality? Evidence from the American Time Use Survey," Working Papers 393, U.S. Bureau of Labor Statistics.
- Haili Zhang & Guohua Zou, 2020. "Cross-Validation Model Averaging for Generalized Functional Linear Model," Econometrics, MDPI, vol. 8(1), pages 1-35, February.
- Laurent Ferrara & Anna Simoni, 2023.
"When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
- Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers 2020-11, University of Paris Nanterre, EconomiX.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers 2007.00273, arXiv.org, revised Sep 2022.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
- Soren Blomquist & Whitney Newey, 2002.
"Nonparametric Estimation with Nonlinear Budget Sets,"
Econometrica, Econometric Society, vol. 70(6), pages 2455-2480, November.
- Blomquist, Sören & Newey, Whitney, 1997. "Nonparametric Estimation of Labor Supply Functions Generated by Piece Wise Linear Budget Constraints," Working Paper Series 1997:24, Uppsala University, Department of Economics.
- Soren Blomquist & Whitney Newey, 1999. "Nonparametric Estimation with Nonlinear Budget Sets," Working papers 99-03, Massachusetts Institute of Technology (MIT), Department of Economics.
- Das, M., 2005. "Instrumental variables estimators of nonparametric models with discrete endogenous regressors," Journal of Econometrics, Elsevier, vol. 124(2), pages 335-361, February.
- Liu, Chu-An, 2012. "A plug-in averaging estimator for regressions with heteroskedastic errors," MPRA Paper 41414, University Library of Munich, Germany.
- I Díaz & O Savenkov & K Ballman, 2018. "Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes," Biometrika, Biometrika Trust, vol. 105(3), pages 723-738.
- Liu, Qingfeng, 2010.
"Generalized Cp Model Averaging for Heteroskedastic Models,"
ビジネス創造センターディスカッション・ペーパー (Discussion papers of the Center for Business Creation)
10252/4334, Otaru University of Commerce.
- Liu, Qingfeng, 2011. "Generalized Cp Model Averaging for Heteroskedastic Models," ビジネス創造センターディスカッション・ペーパー (Discussion papers of the Center for Business Creation) 10252/4544, Otaru University of Commerce.
- Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2015.
"Tree-based censored regression with applications to insurance,"
Working Papers
hal-01141228, HAL.
- Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2016. "Tree-based censored regression with applications in insurance," Post-Print hal-01364437, HAL.
- William R. Emmons & Frank A. Schmid, 2004.
"When for-profits and not-for-profits compete: theory and empirical evidence from retail banking,"
Supervisory Policy Analysis Working Papers
2004-01, Federal Reserve Bank of St. Louis.
- William R. Emmons & Frank A. Schmid, 2004. "When for-profits and not-for-profits compete: theory and empirical evidence from retail banking," Working Papers 2004-004, Federal Reserve Bank of St. Louis.
- Fan, Jianqing & Ke, Yuan & Liao, Yuan, 2021.
"Augmented factor models with applications to validating market risk factors and forecasting bond risk premia,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 269-294.
- Jianqing Fan & Yuan Ke & Yuan Liao, 2016. "Augmented Factor Models with Applications to Validating Market Risk Factors and Forecasting Bond Risk Premia," Papers 1603.07041, arXiv.org, revised Sep 2018.
- Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
- Liu, Chu-An, 2015.
"Distribution theory of the least squares averaging estimator,"
Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
- Liu, Chu-An, 2013. "Distribution Theory of the Least Squares Averaging Estimator," MPRA Paper 54201, University Library of Munich, Germany.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:strimo:v:24:y:2006:i:3:p:21:n:3. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.