Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach
Author
Abstract
Suggested Citation
DOI: 10.1287/ijoc.2020.1019
Download full text from publisher
References listed on IDEAS
- Sebastian Briesemeister & Jörg Rahnenführer & Oliver Kohlbacher, 2012. "No Longer Confidential: Estimating the Confidence of Individual Regression Predictions," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
- Bradley Efron, 2004. "The Estimation of Prediction Error: Covariance Penalties and Cross-Validation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 619-632, January.
- David J. Hand & Keming Yu, 2001. "Idiot's Bayes—Not So Stupid After All?," International Statistical Review, International Statistical Institute, vol. 69(3), pages 385-398, December.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
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.- Theo Dijkstra, 2014. "Ridge regression and its degrees of freedom," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3185-3193, November.
- Sieds, 2012. "Complete Volume LXVI n.1 2012," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 66(1), pages 1-296.
- DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.
- Stefano Marchetti & Maciej Beręsewicz & Nicola Salvati & Marcin Szymkowiak & Łukasz Wawrowski, 2018. "The use of a three‐level M‐quantile model to map poverty at local administrative unit 1 in Poland," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1077-1104, October.
- Hettihewa, Samanthala & Saha, Shrabani & Zhang, Hanxiong, 2018. "Does an aging population influence stock markets? Evidence from New Zealand," Economic Modelling, Elsevier, vol. 75(C), pages 142-158.
- Sascha O. Becker & Luigi Pascali, 2019.
"Religion, Division of Labor, and Conflict: Anti-semitism in Germany over 600 Years,"
American Economic Review, American Economic Association, vol. 109(5), pages 1764-1804, May.
- Sascha O. Becker & Luigi Pascali, 2018. "Religion, Division of Labor and Conflict: Anti-Semitism in Germany over 600 Years," Working Papers 1060, Barcelona School of Economics.
- Sascha O. Becker & Luigi Pascali, 2018. "Religion, division of labor and conflict: Anti-semitism in Germany over 600 years," Economics Working Papers 1619, Department of Economics and Business, Universitat Pompeu Fabra.
- Rajeev D S Raizada & Yune-Sang Lee, 2013. "Smoothness without Smoothing: Why Gaussian Naive Bayes Is Not Naive for Multi-Subject Searchlight Studies," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
- Mendez, Guillermo & Lohr, Sharon, 2011. "Estimating residual variance in random forest regression," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2937-2950, November.
- Yanagihara, Hirokazu & Satoh, Kenichi, 2010. "An unbiased Cp criterion for multivariate ridge regression," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1226-1238, May.
- 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.
- Aletti, Giacomo, 2018. "Generation of discrete random variables in scalable frameworks," Statistics & Probability Letters, Elsevier, vol. 132(C), pages 99-106.
- Zhang, Xinyu & Yu, Jihai, 2018. "Spatial weights matrix selection and model averaging for spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 203(1), pages 1-18.
- Brighton, Henry, 2020. "Statistical foundations of ecological rationality," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-32.
- Chunming Zhang, 2008. "Prediction Error Estimation Under Bregman Divergence for Non‐Parametric Regression and Classification," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 496-523, September.
- Jonathan Bradley & Noel Cressie & Tao Shi, 2015. "Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 1-28, March.
- Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.
- José Luis Preciado Arreola & Daisuke Yagi & Andrew L. Johnson, 2020. "Insights from machine learning for evaluating production function estimators on manufacturing survey data," Journal of Productivity Analysis, Springer, vol. 53(2), pages 181-225, April.
- James Younker, 2022. "Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models," Discussion Papers 2022-19, Bank of Canada.
- Wang, You-Gan & Hin, Lin-Yee, 2010. "Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3359-3370, December.
- Zhang, Bo & Shen, Xiaotong & Mumford, Sunni L., 2012. "Generalized degrees of freedom and adaptive model selection in linear mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 574-586.
More about this item
Keywords
numeric prediction; reliability of individual predictions; machine learning;All these keywords.
Statistics
Access and download statisticsCorrections
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:inm:orijoc:v:34:y:2022:i:1:p:503-521. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.