62
Views
0
CrossRef citations to date
0
Altmetric
Articles

Efficiency of a generalized difference-based weighted mixed ridge estimator in partially linear model

ORCID Icon
Pages 4622-4635 | Received 14 Sep 2020, Accepted 19 Oct 2021, Published online: 05 Nov 2021

References

  • Akdeniz, E., F. Akdeniz, and M. Roozbeh. 2018. A new difference-based weighted mixed Liu estimator in partially linear model. Statistics 52 (6):1309–27.
  • Akdeniz, F., and M. Roozbeh. 2017. Efficiency of the generalized-difference-based weighted mixed almost unbiased two-parameter estimator in partially linear model. Communications in Statistics-Theory and Methods 46 (24):12259–80.
  • Akdeniz, F., and M. Roozbeh. 2019. Generalized difference-based weighted mixed almost unbiased ridge estimator in partially linear models. Statistical Papers 60:1717–39.
  • Akdeniz, F., D. E. Akdeniz, M. Roozbeh, and M. Arashi. 2015. Efficiency of the generalized difference-based Liu estimators in semiparametric regression models with correlated errors. Journal of Statistical Computation and Simulation 85 (1):147–65.
  • Akdeniz, F., M. Roozbeh, E. Akdeniz, and N. M. Khan. 2020. Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models. Communications in Statistics-Theory and Methods. Advance online publication. doi:10.1080/03610926.2020.1814340.
  • Farebrother, R. W. 1976. Further results on the mean square error of ridge regression. Journal of the Royal Statistical Society B 38:248–50.
  • Heckman, N. 1986. Spline smoothing in a partially splined models. Journal of the Royal Statistical Society Series B 48:244–8.
  • Hoerl, A. E., and R. W. Kennard. 1970. Ridge regression: Biased estimation for non-orthogonal problems. Technometrics 12 (1):55–82.
  • Li, Y. 2020. A new stochastic mixed Liu estimator in linear regression model. Communications in Statistics-Theory and Methods 49 (3):726–37.
  • Li, Y. L., and H. Yang. 2014. Efficiency of a stochastic restricted two-parameter estimator in linear regression. Applied Mathematics and Computation 249:371–81.
  • Liang, H. 2006. Estimation in partially linear models and numerical comparisons. Computational Statistics & Data Analysis 50:675–87.
  • Liu, K. J. 2003. Using Liu type estimator to combat multicollinearity. Communications in Statistics-Theory and Methods 32 (5):1009–20.
  • Rao, C., R. Toutenburg, and H. Shalabh. 1995. Linear models: Least squares and alternatives. New York: Springer.
  • Roozbeh, M., M. Arashi, and H. A. Niroumand. 2011. Ridge regression methodology in partial linear models with correlated errors. Journal of Statistical Computation and Simulation 81 (4):517–28.
  • Tabakan, G., and F. Akdeniz. 2010. Difference-based ridge estimator of parameters in partial linear model. Statistical Papers 51 (2):357–68.
  • Trenkler, G. 1984. On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors. Journal of Econometrics 25:179–90.
  • Trenkler, G., and H. Toutenburg. 1990. Mean squared error matrix comparisons between biased estimators: An overview of recent results. Statistical Papers 31:165–79.
  • Wu, J. B. 2018. Improvement of generalized difference-based mixed Liu estimator in partially linear model. Communications in Statistics-Theory and Methods 47 (18):4433–42.
  • Xu, J. W., and H. Yang. 2011a. More on the bias and variance comparisons of the restricted almost unbiased estimators. Communications in Statistics-Theory and Methods 40:4053–64.
  • Xu, J. W., and H. Yang. 2011b. On the restricted almost unbiased estimators in linear regression. Journal of Applied Statistics 38:605–17.
  • Yang, H., and J. Cui. 2011. A stochastic restricted two-parameter estimator in linear regression model. Communications in Statistics - Theory and Methods 40 (13):2318–25.
  • Yang, H., and X. F. Chang. 2010. A new two-parameter estimator in regression model. Communications in Statistics - Theory and Methods 39 (6):923–34.
  • Yatchew, A. 1997. An elementary estimator of the partial linear model. Economics Letters 57:135–43.
  • Yatchew, A. 2000. Scale economies in electricity distribution: A semiparametric analysis. Journal of Applied Econometrics 15 (2):187–210.
  • Yatchew, A. 2003. Semiparametric Regression for the Applied Econometrician. Cambridge: Cambridge University Press.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.