Liu estimator in partly linear regression models with correlated errors

References

• Akdeniz, F., Erol, H. (2003). Mean squared error matrix comparisons of some biased estimators in linear regression. Communications in Statistics - Theory and Methods 32(12):2389–2413.
   Google Scholar
• Buja, A., Hastie, T., Tibshirani, R. (1989). Linear smoothers and additive models. Annals of Statistics 17(2):453–555.
   Google Scholar
• Chen, H. (1988). Convergence rates for parametric components in a partly linear model. Annals of Statistics 16:136–146.
   Google Scholar
• Chen, H., Shiau, J. H. (1991). A two-stage spline smoothing method for partially linear models. Journal of Statistical Planning and Inference 27:187–201.
   Google Scholar
• Duran, E. A., Akdeniz, F., Hu, H. (2011). Efficiency of a Liu-type estimator in semiparametric regression models. Journal of Computational and Applied Mathematics 235(5):1418–1428.
   Google Scholar
• Engle, R. F., Granger, C. W. J., Rice, J., Weiss, A. (1986). Semiparametric estimates of the relation between weather and electricity sales. Journals - American Statistical Association 81:310–320.
   Google Scholar
• Green, P., Jennison, C., Seheult, A. (1985). Analysis of field experiments by least squares smoothing. Journal of the Royal Statistical Society: Series B 47:299–315.
   Google Scholar
• Green, P. J., Silverman, B. W. (1994). Nonparametric Regression and Generalized Linear Models. New York: Chapman and Hall,.
   Google Scholar
• Hoerl, A. E., Kennard, R. W. (1970). Ridge regression: Biased estimation for orthogonal problems. Technometrics 12:55–67.
   Google Scholar
• Hoerl, A. E., Kennard, R. W., Baldwin, K. F. (1975). Ridge Regression: Some Simulation. Communications in Statistics - Theory and Methods 4:105–123.
   Google Scholar
• Hu, H. (2005). Ridge estimation of semiparametric regression model. Journal of Computational and Applied Mathematics 176:215–222.
   Google Scholar
• Hu, H., Rao, S. (2010). Generalized ridge estimation of a semiparametric regression model. Wuhan University Journal of Natural Sciences 15(4):283–286.
   Google Scholar
• Liang, H. (2006). Estimation in partially linear models and numerical comparisons. Computational Statistics & Data Analysis 50(3):675–687.
   Google Scholar
• Liu, K. (1993). A new class of biased estimate in linear regression. Communications in Statistics - Theory and Methods 22:393–402.
   Google Scholar
• Manzan, S., Zerom, D. (2005). Kernel estimation of partially linear additive model. Statistics & Probability Letters 72:313–322.
   Google Scholar
• Mayer, L. S., Willke, T. A. (1973). On biased estimation in linear models. Technometrics 15:497–508.
   Google Scholar
• Rao, C. R., Toutenburg, H., Heumann, S. C. (2008). Linear Models and Genaralizations: Least Squares and Alternatives. 3rd extended ed. Berlin: Springer-Verlag.
   Google Scholar
• Robinson, P. M. (1988). Root-n-consistent semiparametric regression. Econometrica 56:931–954.
   Google Scholar
• Roozbeh, M., Arashi, M., Niroumand, H. A. (2011). Ridge regression methodology in partial linear models with correlated errors. Journal of Statistical Computation and Simulation 81(4):517–528.
   Google Scholar
• Speckman, P. (1988). Kernel smoothing in partial linear models. Journal of the Royal Statistical Society: Series B 50(3):413–436.
   Google Scholar
• Trenkler, G. (1984). On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors. Journal of Economic 25:179–190.
   Google Scholar
• Trenkler, G., Toutenburg, H. (1990). Mean square matrix comparisons between biased estimators-an overview of recent results. Statistical Papers 31:165–179.
   Google Scholar
• Wahba, G. (1984). Cross validated spline methods for the estimation of multivariate functions from data on functionals. Statistics: An appraisal. In: Proceedings of the 50th Anniversary Conference Iowa State Statistical Laboratory, David, H. A., ed. Iowa State University Press.
   Google Scholar