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Machine Learning and Other Topics

Mixed Lasso estimator for stochastic restricted regression models

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Pages 2795-2808 | Received 31 May 2020, Accepted 18 Apr 2021, Published online: 04 May 2021

References

  • D. Aigner, C.A.K. Lovell, and P. Schmidt, Formulation and estimation of stochastic frontier production function models, J. Econ. 6 (1977), pp. 21–37.
  • A.C. Aitken, IV.—On least squares and linear combination of observations, Proc. Roy. Soc. Edinb. 55 (1936), pp. 42–48.
  • J. Bai, and P. Perron, Computation and analysis of multiple structural change models, J. Appl. Econ. 18 (2003), pp. 1–22.
  • M. Caner, and K. Knight, An alternative to unit root tests: Bridge estimators differentiate between nonstationary versus stationary models and select optimal lag, J. Stat. Plan. Infer. 143 (2013), pp. 691–715.
  • J. Chen, and S. Deng, Detection of copy number variation regions using the DNA-sequencing data from multiple profiles with correlated structure, J. Comput. Biol. 25 (2018), pp. 1128–1140.
  • R.N. Conway, and R.C. Mittelhammer, The theory of mixed estimation in econometric modeling, Stud. Econ. Finance 10 (1986), pp. 79–120.
  • B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann. Stat. 32 (2004), pp. 407–451.
  • J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, Pathwise coordinate optimization, Ann. Appl. Stat. 1 (2007), pp. 302–332.
  • W.J. Fu, Penalized regressions: The bridge versus the lasso, J. Comp. Graph Stat. 7 (1998), pp. 397–416.
  • W.H. Greene, Econometric Analysis, 5th ed., Pearson Education Inc, Upper Saddle River, NJ, 2003.
  • H. Guler, and E. Ozgur Guler, Sparsely restricted penalized estimators, Commun. Stat. - Theor. Meth. 50 (2021), pp. 1656–1670.
  • G. Hildebrand, and T.C. Liu, Manufacturing Production Functions in the U.S., 1957,1st edn, Cornell University Press, Ithaca, NY, 1965.
  • J.Z. Jia, K. Rohe, and B. Yu, The lasso under Poisson-like heteroscedasticity, Stat. Sin. 23 (2013), pp. 99–118.
  • K. Knight, and W. Fu, Asymptotics for lasso-type estimators, Ann. Stat. 28 (2000), pp. 1356–1378.
  • J. Lee, and J. Chen, A penalized regression approach for DNA copy number study using the sequencing data, Stat. Appl. Genet. Mol. Biol. 18 (2019), pp. 1–14.
  • T. Lee, C. Park, and Y.J. Yoon, Bridge estimation for linear regression models with mixing properties, Austral. New Zealand J. Stat. 56 (2014), pp. 283–302.
  • B. Li, and Q.Z. Yu, Robust and sparse bridge regression, Stat. Its Inter. 2 (2009), pp. 481–491.
  • M. Norouzirad, and M. Arashi, Preliminary test and stein-type shrinkage LASSO-based estimators, Sort-Stat. Operat. Res. Trans. 42 (2018), pp. 45–57.
  • M. Norouzirad, M. Arashi, and A.K.M.E. Saleh, Restricted LASSO and double shrinking, 2015, pp. 1–20. arXiv:1505.02913.
  • M.R. Osborne, B. Presnell, and B.A. Turlach, A new approach to variable selection in least squares problems, IMA J. Numer. Anal. 20 (2000), p. 389.
  • C. Park, and Y.J. Yoon, Bridge regression: Adaptivity and group selection, J. Stat. Plan Infer. 141 (2011), pp. 3506–3519.
  • P. Perron, The great crash, the oil price shock, and the unit-root hypothesis, Econometrica 57 (1989), pp. 1361–1401.
  • H. Theil, On the use of incomplete prior information in regression analysis, J. Am. Stat. Assoc. 58 (1963), pp. 401–414.
  • H. Theil, and A.S. Goldberger, On pure and mixed statistical estimation in economics, Int. Econ. Rev. 2 (1961), pp. 65–78.
  • R. Tibshirani, Regression shrinkage and selection via the lasso, J. Roy. Stat. Soc. Ser. B (Methodol.) 58 (1996), pp. 267–288.
  • G. Trenkler, On the performance of biased-estimators in the linear-regression model with correlated or heteroscedastic errors, J. Econ. 25 (1984), pp. 179–190.
  • H. Wang, B. Li, and C. Leng, Shrinkage tuning parameter selection with a diverging number of parameters, J. Roy. Stat. Soc.: Ser. B (Stat. Method.) 71 (2009), pp. 671–683.
  • T.T. Wu, and K. Lange, Coordinate descent algorithms for lasso penalized regression, Ann. Appl. Stat. 2 (2008), pp. 224–244.
  • H. Zou, and T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B (Stat. Method.) 67 (2005), pp. 301–320.

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