IDEAS home Printed from https://ideas.repec.org/r/oup/biomet/v92y2005i4p937-950.html
   My bibliography  Save this item

Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Agovino, Massimiliano & Cerciello, Massimiliano & Musella, Gaetano, 2021. "Campania and cancer mortality: An inseparable pair? The role of environmental quality and socio-economic deprivation," Social Science & Medicine, Elsevier, vol. 287(C).
  2. Yingying Fan & Cheng Yong Tang, 2013. "Tuning parameter selection in high dimensional penalized likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 531-552, June.
  3. Lee, Yoonseok & Phillips, Peter C.B., 2015. "Model selection in the presence of incidental parameters," Journal of Econometrics, Elsevier, vol. 188(2), pages 474-489.
  4. Zhang, Xinyu & Liu, Chu-An, 2023. "Model averaging prediction by K-fold cross-validation," Journal of Econometrics, Elsevier, vol. 235(1), pages 280-301.
  5. Chirwa, Themba G. & Odhiambo, Nicholas M., 2016. "What Drives Long-Run Economic Growth? Empirical Evidence from South Africa," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 69(4), pages 429-456.
  6. Constantino, Michel & Candido, Osvaldo & Tabak, Benjamin M. & da Costa, Reginaldo Brito, 2017. "Modeling stochastic frontier based on vine copulas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 595-609.
  7. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
  8. Overholser, Rosanna & Xu, Ronghui, 2014. "Effective degrees of freedom and its application to conditional AIC for linear mixed-effects models with correlated error structures," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 160-170.
  9. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
  10. Samuel Müller & Alan H. Welsh, 2010. "On Model Selection Curves," International Statistical Review, International Statistical Institute, vol. 78(2), pages 240-256, August.
  11. Healy, Paul J. & Park, Hyoeun, 2023. "Model selection accuracy in behavioral game theory: A simulation," European Economic Review, Elsevier, vol. 152(C).
  12. Emura, Takeshi & Shiu, Shau-Kai, 2014. "Estimation and model selection for left-truncated and right-censored lifetime data with application to electric power transformers analysis," MPRA Paper 57528, University Library of Munich, Germany.
  13. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
  14. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
  15. Erhard Reschenhofer & David Preinerstorfer & Lukas Steinberger, 2013. "Non-monotonic penalizing for the number of structural breaks," Computational Statistics, Springer, vol. 28(6), pages 2585-2598, December.
  16. Megan L. Neely & Howard D. Bondell & Jung-Ying Tzeng, 2015. "A penalized likelihood approach for investigating gene–drug interactions in pharmacogenetic studies," Biometrics, The International Biometric Society, vol. 71(2), pages 529-537, June.
  17. Zhigeng Geng & Sijian Wang & Menggang Yu & Patrick O. Monahan & Victoria Champion & Grace Wahba, 2015. "Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study," Biometrics, The International Biometric Society, vol. 71(1), pages 53-62, March.
  18. Worthington, Andrew C. & Zelenyuk, Valentin, 2018. "Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese bankingAuthor-Name: Du, Kai," European Journal of Operational Research, Elsevier, vol. 265(2), pages 748-764.
  19. Fitzpatrick, Matthew & Stewart, Michael, 2022. "Asymptotics for Markov chain mixture detection," Econometrics and Statistics, Elsevier, vol. 22(C), pages 56-66.
  20. Kwon, Sunghoon & Lee, Sangin & Kim, Yongdai, 2015. "Moderately clipped LASSO," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 53-67.
  21. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, vol. 142(1), pages 201-211, January.
  22. HAEDO, Christian & MOUCHART , Michel & ,, 2013. "Specialized agglomerations with areal data: model and detection," LIDAM Discussion Papers CORE 2013060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  23. Pötscher, Benedikt M., 2007. "Confidence Sets Based on Sparse Estimators Are Necessarily Large," MPRA Paper 5677, University Library of Munich, Germany.
  24. Kwon, Sunghoon & Oh, Seungyoung & Lee, Youngjo, 2016. "The use of random-effect models for high-dimensional variable selection problems," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 401-412.
  25. Xinyu Zhang & Dalei Yu & Guohua Zou & Hua Liang, 2016. "Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1775-1790, October.
  26. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
  27. Ian L. Dryden & Kwang-Rae Kim & Huiling Le, 2019. "Bayesian Linear Size-and-Shape Regression with Applications to Face Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 83-103, February.
  28. Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.
  29. Ross, Gordon J., 2013. "Modelling financial volatility in the presence of abrupt changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 350-360.
  30. Heng Lian & Peng Lai & Hua Liang, 2013. "Partially Linear Structure Selection in Cox Models with Varying Coefficients," Biometrics, The International Biometric Society, vol. 69(2), pages 348-357, June.
  31. Howard D. Bondell & Arun Krishna & Sujit K. Ghosh, 2010. "Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models," Biometrics, The International Biometric Society, vol. 66(4), pages 1069-1077, December.
  32. Kai Du & Allan O’Connor, 2021. "Examining economic complexity as a holistic innovation system effect," Small Business Economics, Springer, vol. 56(1), pages 237-257, January.
  33. Hutter, Marcus & Tran, Minh-Ngoc, 2010. "Model selection with the Loss Rank Principle," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1288-1306, May.
  34. Léna Carel & Pierre Alquier, 2021. "Simultaneous dimension reduction and clustering via the NMF-EM algorithm," 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. 15(1), pages 231-260, March.
  35. Binbing Yu & Lan Huang & Ram C. Tiwari & Eric J. Feuer & Karen A. Johnson, 2009. "Modelling population‐based cancer survival trends by using join point models for grouped survival data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 405-425, April.
  36. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
  37. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
  38. Paolo Vidoni, 2015. "Estimating the Kullback–Liebler risk based on multifold cross-validation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(4), pages 510-540, November.
  39. Ruoyao Shi, 2021. "An Averaging Estimator for Two Step M Estimation in Semiparametric Models," Working Papers 202105, University of California at Riverside, Department of Economics.
  40. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
  41. Tim J. Boonen & Hong Li, 2017. "Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach," Demography, Springer;Population Association of America (PAA), vol. 54(5), pages 1921-1946, October.
  42. Zhang, Yongli & Yang, Yuhong, 2015. "Cross-validation for selecting a model selection procedure," Journal of Econometrics, Elsevier, vol. 187(1), pages 95-112.
  43. Xiaoyi Zhu & Yuhong Yang, 2015. "Variable selection after screening: with or without data splitting?," Computational Statistics, Springer, vol. 30(1), pages 191-203, March.
  44. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
  45. Kim, Donggyu & Wang, Yazhen, 2017. "Hypothesis tests for large density matrices of quantum systems based on Pauli measurements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 31-51.
  46. 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.
  47. Léna CAREL & Pierre ALQUIER, 2017. "Simultaneous Dimension Reduction and Clustering via the NMF-EM Algorithm," Working Papers 2017-38, Center for Research in Economics and Statistics.
  48. Hansheng Wang & Bo Li & Chenlei Leng, 2009. "Shrinkage tuning parameter selection with a diverging number of parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 671-683, June.
  49. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
  50. Andrew J. Collins & Sheida Etemadidavan & Wael Khallouli, 2020. "Generating Empirical Core Size Distributions of Hedonic Games using a Monte Carlo Method," Papers 2007.12127, arXiv.org.
  51. Yonekura, Shouto & Beskos, Alexandros & Singh, Sumeetpal S., 2021. "Asymptotic analysis of model selection criteria for general hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 164-191.
  52. Matthew Cefalu & Francesca Dominici & Nils Arvold & Giovanni Parmigiani, 2017. "Model averaged double robust estimation," Biometrics, The International Biometric Society, vol. 73(2), pages 410-421, June.
  53. Astegiano, Paola & Akinc, Deniz & Himpe, Willem & Tampère, Chris M.J. & Vandebroek, Martina, 2017. "Quantifying the explanatory power of mobility-related attributes in explaining vehicle ownership decisions," Research in Transportation Economics, Elsevier, vol. 66(C), pages 2-11.
  54. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
  55. William Kengne, 2023. "On consistency for time series model selection," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 437-458, July.
  56. Lee, Eun Ryung & Park, Byeong U., 2012. "Sparse estimation in functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 1-17.
  57. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.
  58. Zachary K. Collier & Haobai Zhang & Bridgette Johnson, 2021. "Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches," Evaluation Review, , vol. 45(6), pages 309-333, December.
  59. Saburi, S. & Chino, N., 2008. "A maximum likelihood method for an asymmetric MDS model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4673-4684, June.
  60. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
  61. Mori, Yuichi & Suzuki, Taiji, 2018. "Generalized ridge estimator and model selection criteria in multivariate linear regression," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 243-261.
  62. Beyá-Marshall, Víctor & Arcos, Emilia & Seguel, Óscar & Galleguillos, Mauricio & Kremer, Cristián, 2022. "Optimal irrigation management for avocado (cv. 'Hass') trees by monitoring soil water content and plant water status," Agricultural Water Management, Elsevier, vol. 271(C).
  63. Gordon J. Ross, 2012. "Modeling Financial Volatility in the Presence of Abrupt Changes," Papers 1212.6016, arXiv.org.
  64. Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
  65. Bingkai Wang & Brian S. Caffo & Xi Luo & Chin‐Fu Liu & Andreia V. Faria & Michael I. Miller & Yi Zhao & for the Alzheimer's Disease Neuroimaging Initiative*, 2022. "Regularized regression on compositional trees with application to MRI analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 541-561, June.
  66. Ruoyao Shi & Zhipeng Liao, 2018. "An Averaging GMM Estimator Robust to Misspecification," Working Papers 201803, University of California at Riverside, Department of Economics.
  67. Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.
  68. Lan Liu & Wei Li & Zhihua Su & Dennis Cook & Luca Vizioli & Essa Yacoub, 2022. "Efficient estimation via envelope chain in magnetic resonance imaging‐based studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 481-501, June.
  69. Kira Alhorn & Holger Dette & Kirsten Schorning, 2021. "Optimal Designs for Model Averaging in non-nested Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 745-778, August.
  70. Wenjing Yang & Yuhong Yang, 2017. "Toward an objective and reproducible model choice via variable selection deviation," Biometrics, The International Biometric Society, vol. 73(1), pages 20-30, March.
  71. Howard D. Bondell & Brian J. Reich, 2009. "Simultaneous Factor Selection and Collapsing Levels in ANOVA," Biometrics, The International Biometric Society, vol. 65(1), pages 169-177, March.
  72. Georgios Sermpinis & Serafeim Tsoukas & Ping Zhang, 2019. "What influences a bank's decision to go public?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1464-1485, October.
  73. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.