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Model Selection and the Principle of Minimum Description Length

Citations

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Cited by:

  1. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
  2. Ivan Chang, Yuan-Chin & Huang, Yufen & Huang, Yu-Pai, 2010. "Early stopping in L2Boosting," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2203-2213, October.
  3. Miloslavsky, Maja & van der Laan, Mark J., 2003. "Fitting of mixtures with unspecified number of components using cross validation distance estimate," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 413-428, January.
  4. Njindan Iyke, Bernard, 2015. "Macro Determinants of the Real Exchange Rate in a Small Open Small Island Economy: Evidence from Mauritius via BMA," MPRA Paper 68968, University Library of Munich, Germany.
  5. Branimir Jovanovic, 2017. "Growth forecast errors and government investment and consumption multipliers," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(1), pages 83-107, January.
  6. Rissanen, Jorma & Roos, Teemu & Myllymäki, Petri, 2010. "Model selection by sequentially normalized least squares," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 839-849, April.
  7. Sylvain Barde, 2017. "A Practical, Accurate, Information Criterion for Nth Order Markov Processes," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 281-324, August.
  8. Branimir Jovanovic, 2012. "How Policy Actions Affect Short-term Post-crisis Recovery?," CEIS Research Paper 253, Tor Vergata University, CEIS, revised 05 Oct 2012.
  9. David Kaplan, 2021. "On the Quantification of Model Uncertainty: A Bayesian Perspective," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 215-238, March.
  10. Falk, Carl F. & Muthukrishna, Michael, 2021. "Parsimony in model selection: tools for assessing fit propensity," LSE Research Online Documents on Economics 110856, London School of Economics and Political Science, LSE Library.
  11. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
  12. Paul L. Bowen & Robert A. O'Farrell & Fiona H. Rohde, 2009. "An Empirical Investigation of End-User Query Development: The Effects of Improved Model Expressiveness vs. Complexity," Information Systems Research, INFORMS, vol. 20(4), pages 565-584, December.
  13. Jingwen Gu & Ao Yuan’s & Ming T Tan, 2018. "Partial Variable Selection and Its’ Applications in Biostatistics," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(1), pages 7-12, April.
  14. Joris Mulder & James O. Berger & Víctor Peña & M. J. Bayarri, 2021. "On the prevalence of information inconsistency in normal linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 103-132, March.
  15. Makalic, Enes & Schmidt, Daniel F., 2009. "Minimum Message Length shrinkage estimation," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1155-1161, May.
  16. Brian Hanlon & Catherine Forbes, 2002. "Model Selection Criteria for Segmented Time Series from a Bayesian Approach to Information Compression," Monash Econometrics and Business Statistics Working Papers 8/02, Monash University, Department of Econometrics and Business Statistics.
  17. Seongkyoon Jeong & Jae Young Choi, 2012. "The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 719-735, June.
  18. Bernard Njindan Iyke, 2018. "Macro Determinants Of The Real Exchange Rate In A Small Open Small Island Economy:Evidence From Mauritius Via Bma," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 21(1), pages 1-24, July.
  19. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
  20. Massimo Marinacci, 2015. "Model Uncertainty," Journal of the European Economic Association, European Economic Association, vol. 13(6), pages 1022-1100, December.
  21. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
  22. Paul Hofmarcher & Jesús Crespo Cuaresma & Bettina Grün & Kurt Hornik, 2015. "Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 133-144, March.
  23. Jan G. De Gooijer & Ao Yuan, 2008. "MDL Mean Function Selection in Semiparametric Kernel Regression Models," Tinbergen Institute Discussion Papers 08-046/4, Tinbergen Institute.
  24. Linda Mhalla & Valérie Chavez‐Demoulin & Debbie J. Dupuis, 2020. "Causal mechanism of extreme river discharges in the upper Danube basin network," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 741-764, August.
  25. Yue, Mu & Li, Jialiang & Cheng, Ming-Yen, 2019. "Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 222-234.
  26. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
  27. Ching-Kang Ing, 2005. "Accumulated Prediction Errors, Information Criteria And Optimal Forecasting For Autoregressive Time Series," Econometrics 0503020, University Library of Munich, Germany.
  28. repec:hal:spmain:info:hdl:2441/5fafm6me7k8omq5jbo61urqq27 is not listed on IDEAS
  29. Karol Szafranek & Marek Kwas & Grzegorz Szafrański & Zuzanna Wośko, 2020. "Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach," Energies, MDPI, vol. 13(23), pages 1-23, November.
  30. Motegi, Ryosuke & Seki, Yoichi, 2023. "SMLSOM: The shrinking maximum likelihood self-organizing map," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
  31. Md Atikur Rahman Khan & D.S. Poskitt, 2010. "Description Length Based Signal Detection in singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 13/10, Monash University, Department of Econometrics and Business Statistics.
  32. Leitenstorfer, Florian & Tutz, Gerhard, 2007. "Knot selection by boosting techniques," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4605-4621, May.
  33. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
  34. Brockwell, P. J. & Dahlhaus, R., 2004. "Generalized Levinson-Durbin and Burg algorithms," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 129-149.
  35. Magkonis, Georgios & Zekente, Kalliopi-Maria, 2020. "Inflation-output trade-off: Old measures, new determinants?," Journal of Macroeconomics, Elsevier, vol. 65(C).
  36. Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
  37. In-Koo Cho & Ken Kasa, 2012. "Model Validation and Learning," Discussion Papers dp12-07, Department of Economics, Simon Fraser University.
  38. Marcos Prates & Renato Assunção & Marcelo Costa, 2012. "Flexible scan statistic test to detect disease clusters in hierarchical trees," Computational Statistics, Springer, vol. 27(4), pages 715-737, December.
  39. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
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