A Scalable Frequentist Model Averaging Method

References

• Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in 2nd International Symposium on Information Theory, eds. (B. N. Petrov and F. Cźaki), pp. 267–281, Budapest: Akademiai Kaidó.
   Google Scholar
• Ando, T. and Li, K. (2014), “A Model-Averaging Approach for High-Dimensional Regression,” Journal of American Statistical Association, 109, 254–265. DOI: 10.1080/01621459.2013.838168.
   Web of Science ®Google Scholar
• Bai, J. (2003), “Inferential Theory for Factor Models of Large Dimensions,” Econometrica, 71, 135–171. DOI: 10.1111/1468-0262.00392.
   Web of Science ®Google Scholar
• Breheny, P., and Huang, J. (2011), “Coordinate Descent Algorithms for Nonconvex Penalized Regression, with Applications to Biological Feature Selection,” Annals of Applied Statistics, 5, 232–253.
   PubMed Web of Science ®Google Scholar
• Buckland, S., Burnham, K., and Augustin, N. (1997), “Model Selection: An Integral Part of Inference,” Biometrics, 53, 603–618. DOI: 10.2307/2533961.
   Web of Science ®Google Scholar
• Charkhi, A., Claeskens, G., and Hansen, B. E. (2016), “Minimum Mean Squared Error Model Averaging in Likelihood Models,” Statistica Sinica, 26, 809–840. DOI: 10.5705/ss.202014.0067.
   Web of Science ®Google Scholar
• Claeskens, G. (2012), “Focused Estimation and Model Averaging with Penalization Methods, an Overview,” Statistica Neerlandica, 66, 272–287. DOI: 10.1111/j.1467-9574.2012.00514.x.
   Web of Science ®Google Scholar
• Clyde, M., Desimone, H., and Parmigiani, G. (1996), “Prediction via Orthogonalized Model Mixing,” Journal of the American Statistical Association, 91, 1197–1208. DOI: 10.1080/01621459.1996.10476989.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Noconcave Penalized Likelihood and its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360. DOI: 10.1198/016214501753382273.
   Web of Science ®Google Scholar
• Fan, J., and Lv, J. (2008), “Sure Independence Screening for Ultrahigh Dimensional Feature Space,” (with Discussion), Journal of the Royal Statistical Society, Series B, 70, 849–911. DOI: 10.1111/j.1467-9868.2008.00674.x.
   PubMed Web of Science ®Google Scholar
• Hansen, B. (2007), “Least Squares Model Averaging,” Econometrica, 75, 1175–1189. DOI: 10.1111/j.1468-0262.2007.00785.x.
   Web of Science ®Google Scholar
• Hansen, B., and Racine, J. (2012), “Jackknife Model Averaging,” Journal of Econometrics, 167, 38–46. DOI: 10.1016/j.jeconom.2011.06.019.
   Web of Science ®Google Scholar
• Hjort, N., and Claeskens, G. (2003), “Frequentist Model Average Estimators,” Journal of American Statistical Association, 98, 879–899. DOI: 10.1198/016214503000000828.
   Web of Science ®Google Scholar
• Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T. (1999), “Bayesian Model Averaging: A Tutorial,” Statistical Science, 14, 382–417.
   Web of Science ®Google Scholar
• Jolliffe, I. T. (1982), “A Note on the Use of Principal Components in Regression,” Journal of the Royal Statistical Society, Series C, 31, 300–303. DOI: 10.2307/2348005.
   Web of Science ®Google Scholar
• Leung, G., and Barron, A. R. (2006), “Information Theory and Mixing Least-Squares Regressions,” IEEE Transactions on Information Theory, 52, 3396–3410. DOI: 10.1109/TIT.2006.878172.
   Web of Science ®Google Scholar
• Liang, H., Zou, G., Wan, A., and Zhang, X. (2011), “Optimal Weight Choice for Frequentist Model Average Estimators,” Journal of American Statistical Association, 106, 1053–1066. DOI: 10.1198/jasa.2011.tm09478.
   Web of Science ®Google Scholar
• Magnus, J., and Durbin, J. (1999), “Estimation of Regression Coefficients of Interest When other Regression Coefficients are of no Interest,” Econometrica, 67, 639–643. DOI: 10.1111/1468-0262.00040.
   Web of Science ®Google Scholar
• Park, S. H. (1981), “Collinearity and Optimal Restrictions on Regression Parameters for Estimating Responses,” Technometrics, 23, 289–295. DOI: 10.2307/1267793.
   Web of Science ®Google Scholar
• Raftery, A., Madigan, D., and Hoeting, J. (1997), “Bayesian Model Averaging for Linear Regression Models,” Journal of American Statistical Association, 92, 179–191. DOI: 10.1080/01621459.1997.10473615.
   Web of Science ®Google Scholar
• Wooldridge, D. (2003), Introductory Econometrics, Mason, OH: Thompson South-Western.
   Google Scholar
• Yang, Y. (2001), “Adaptive Regression by Mixing,” Journal of American Statistical Association, 96, 574–588. DOI: 10.1198/016214501753168262.
   Web of Science ®Google Scholar
• Yuan, Z., and Yang, Y. (2005), “Combining Linear Regression Models: When and How?” Journal of American Statistical Association, 100, 1202–1214. DOI: 10.1198/016214505000000088.
   Web of Science ®Google Scholar
• Zhang, C. (2010), “Nearly Unbiased Variable Selection under Minimax Concave Penalty,” The Annals of Statistics, 38, 894–942. DOI: 10.1214/09-AOS729.
   Web of Science ®Google Scholar
• Zhu, L., Li, L., Li, R., and Zhu, L. (2011), “Model-Free Feature Screening for Ultrahigh-Dimensional Data,” Journal of the American Statistical Association, 106, 1464–1475. DOI: 10.1198/jasa.2011.tm10563.
   PubMed Web of Science ®Google Scholar
• Zhu, L.-P. (2011), “Extending the Scope of Inverse Regression Methods in Sufficient Dimension Reduction,” Communications in Statistics. Theory and Methods, 40, 84–95. DOI: 10.1080/03610920903350531.
   Web of Science ®Google Scholar