Shrinkage Difference-Based Liu Estimators In Semiparametric Linear Models

  • Hadi Emami Department of Statistics, University of Zanjan, Iran.
  • Sara Kiani Department of Statistics, University of Zanjan, Iran.
Keywords: Linear restriction, Multicollinearity, Preliminary test, Restricted Liu estimator, Risk function, Stein-type shrinkage

Abstract

In this article, under a multicollinearity setting, we define difference-based Liu and non-Liu type shrinkage estimators along with their positive parts in the semiparametric linear model, when the errors are dependent and some nonstochastic linear restrictions are imposed. We derive the biases and exact risk expressions of these estimators and obtain the region of optimality of each estimator. Also, necessary and sufficient conditions, for the superiority of the difference-based Liu estimator over its counterpart, for choosing the Liu parameter d are established. Finally, we illustrate the performance of these estimators with a simulation study.

Author Biographies

Hadi Emami, Department of Statistics, University of Zanjan, Iran.
Department of Statistics, University of Zanjan, Iran.
Sara Kiani, Department of Statistics, University of Zanjan, Iran.
Department of Statistics, University of Zanjan, Iran.

References

F. Akdeniz, E. Akdeniz Duran, M. Roozbeh, M. Arashi, Efficiency of the generalized difference-based Liu estimators in semiparametric regression models with correlated errors, Journal of Statistical Computation and Simulation, 85(1), 147-165, 2015.

M. Arashi, Preliminary test and Stein estimators in simultaneous linear equations, J Linear Algebra Appl. 436, 5: 1195-1211, 2012.

M. Arashi, B.M. Golam Kibria, M. Norouzirad, S. Nadarajah, Improved preliminary test and Stein-rule Liu estimators for the ill-conditioned elliptical linear regression model, J. Multivariate Anal. 124, 53-74, 2014.

M. Arashi, M. Roozbeh, Some improved estimation strategies in high-dimensional semiparametric regression models with application to riboflavin production data, Stat. Paper, 1-20, 2016.

N. Benda, Pre-test estimation and design in the linear model, Statist. Plann. Inference, 52: (2), 225-240, (1996).

L.D.Brown, L.Wang, T.T.Cai, A difference-based approach to the semiparametric partial linear model, Electron.J.Stat.5, 619-64, 2011.

D.G. Gibbons, A, Simulation study of some ridge estimators, J. Amer. Statist. Assoc. 76, 131139, 1981.

W. Hrdle, H. Liang, J. Gao,, Partially Linear Models, Physika Verlag, Heidelberg, 2000.

A.E. Hoerl, R.W. Kennard, Ridge regression: biased estimation in non-orthogonal problems, Technometrics, 12: (3), 55-67,1970.

G.G. Judge, M.E. Bock, The Statistical Implications of Pre-test and Stein-rule Estimators in Econometrics, North-Holland Publishing Company, Amsterdam, 1978.

K. Liu, A new class of biased estimate in linear regression, Comm.Statist.Theory Methods, 22:2, 393-402, 1993.

K. Liu, Using Liu-type estimator to combat collinearity, Comm. Statist. Theory Methods, 32: (5),1009-1020, 2003.

M. Norouzirad, M. Arashi, Preliminary test and Stein-type shrinkage ridge estimators in robust regression, Stat. Paper, 1-34, 2017.

G.C. McDonald, D.I. Galarneau, A Monte Carlo evaluation of some ridge-type estimators, J. Amer. Statist. Assoc. 70, 407-416, 1975.

M.Muller, Semiparametric Extensions to Generalized Linear Models, Habilitationsschrift,2000.

C.R, Rao, H.Toutenburg, Shalabh, C.Heumann, Linear Models:Least Squares and Alternatives, Springer, Berlin, 2008.

M. Roozbeh, Shrinkage ridge estimators in semiparametric regression models, J.Multivariate Anal.136, 56-74, 2015.

M. Roozbeh, M. Arashi, Feasible ridge estimator in partially linear models, Journal of Multivariate, 116, 35-44, 2013.

D. Ruppert, SJ. Sheather, MP. Wand, An effective bandwidth selector for local least squares regression, Journal of the American Statistical Association 90: 432, 1257-1270, 1995.

A.K.Md.E. Saleh, Theory of Preliminary Test and Stein-type Estimation with Applications, John Wiley, New York, 2006.

A.K.Md.E. Saleh, B.M.G. Kibria, Performances of some new preliminary test ridge regression estimators and their properties, Comm. Statist. Theory Methods, 22: (10), 2747-2764, 1993.

C. Stein, Inadmissibility of the usual estimator for the mean of a multivariate normal distribution, in: Proceedings of the Third Berkeley Symposium, vol. 1, pp. 197206, 1956.

P.A.V.B. Swamy, J.S. Mehta, A note on minimum average risk estimator for coefficients in linear models, Comm. Statist. Theory Methods, 6,1161-1186, 1977.

P.A.V.B. Swamy, J.S. Mehta, P.N. Rappoport, Two methods of evaluating Hoerl and Kennards ridge regression, Comm. Statist. Theory Methods, 6,1133-1155, 1978.

J.B. Wu, Y. Asr, A weighted stochastic restricted ridge estimator in partially linear model, Communications in Statistics-Theory and Methods, 46(18), 9274-9283, 2017.

A.Yatchew, Semiparametric Regression for the Applied Econometrican, Cambridge University Press,Cambridge, 2003.

B. Yuzbasi, S.E. Ahmed, D. Aydin, Ridge-type pretest and shrinkage estimations in partially linear models, Statistical papers. https://doi.org/10.1007/s00362-017-0967-8,2017.

B. Yuzbasi, Y. Asar, S.M. Sk, A. Demiralp, Improving estimations in quantile regression model with autoregressive errors, Thermal Science, https://doi.org/10.2298/TSCI170612275Y, 2018.

J. Li, W. Zhang, Z. Wu, Optimal zone for bandwidth selection in semiparametric models, J. Nonparametr.Stat. 23, 701-717, 2011.

Z. Zhong, Hu Yang, Ridge estimation to the restricted linear model, Comm. Statist. Theory Methods, 36:11, 2099-2115, (2007).

Published
2018-08-19
How to Cite
Emami, H., & Kiani, S. (2018). Shrinkage Difference-Based Liu Estimators In Semiparametric Linear Models. Statistics, Optimization & Information Computing, 6(3), 354-372. https://doi.org/10.19139/soic.v6i3.576
Section
Research Articles