References
- Chatterjee, S., and A. S. Hadi. 1988. Sensitivity analysis in linear regression. New York: Wiley.
- Farebrother, R. W. 1976. Further results on the mean square error of ridge regression. Journal of the Royal Statistical Society 38 (3):248–50. doi:10.1111/j.2517-6161.1976.tb01588.x.
- Fung, W. K., X. P. Zhong, and B. C. Wei. 2003. On etimation and influence diagnostics in linear mixed measurement error models. American Journal of Mathematical and Management Sciences 23:37–59.
- Ghapani, F., and B. Babadi. 2018. Mixed Liu estimator in linear measurement error models. Communications in Statistics – Theory and Methods 47 (7):1561–70. doi:10.1080/03610926.2017.1321768.
- Ghapani, F., A. R. Rasekh, and B. Babadi. 2015. Detection of outliers and influential observations in linear ridge measurement error models with stochastic linear restrictions. Journal of Sciences, Islamic Republic of Iran 26 (4):355–66.
- Ghapani, F., A. R. Rasekh, and B. Babadi. 2018. The weighted ridge estimator in stochastic restricted linear measurement error models. Statistical Papers 59 (2):709–23. doi:10.1007/s00362-016-0786-3.
- Hoerl, A. E., and R. W. Kennard. 1970. Ridge regression: Biased estimation for non-orthogonal problems. Technometrics 12 (1):69–82. doi:10.1080/00401706.1970.10488635.
- Li, Y. and H. Yang. 2011. A new ridge–type estimator in stochastic restricted linear regression. Statistics 45 (2):123–30. doi:10.1080/02331880903573153.
- Li, Y. and H. Yang. 2014. Efficiency of a stochastic restricted two-parameter estimator in linear regression. Journal of Applied Mathematics and Computing. 249:371–81. doi:10.1016/j.amc.2014.10.011.
- Liu, K. 1993. A new class of biased estimate in linear regression. Communications in Statistics Theory and Methods 22:393–402.
- McDonald, C., and D. A. Galarneau. 1975. A Monte Carlo evaluation of some ridge-type estimators. Journal of the American Statistical Association 70 (350):407–16. doi:10.1080/01621459.1975.10479882.
- Nakamura, T. 1990. Corrected score function for errors-in-variables models: Methodology and application to generalized linear models. Biometrika 77 (1):127–37. doi:10.1093/biomet/77.1.127.
- Özbay, N., and S. Kaçıranlar. 2018. Estimation in a linear regression model with stochastic linear restrictions: A new two-parameter-weighted mixed estimator. Journal of Statistical Computation and Simulation 88 (9):1669–83. doi:10.1080/00949655.2018.1442836.
- Özkale, M. R., and S. Kaçıranlar. 2007. The restricted and unrestricted two parameter estimators. Communications in Statistics – Theory and Methods 36 (15):2707–25. doi:10.1080/03610920701386877.
- Rao, C. R., H. Toutenburg, and C. S. Heumann. 2008. Linear models and generslizations. Berlin: Springer.
- Rasekh, A. R. 2006. Local influence in measurement error models with ridge estimate. Computational Statistics and Data Analysis. 50 (10):2822–34. doi:10.1016/j.csda.2005.04.022.
- Sakallıoğlu, S., and S. Kacıranlar. 2008. A new biased estimator based on ridge estimation. Statistical Papers 49 (4):669–89. doi:10.1007/s00362-006-0037-0.
- Stein, C. 1956. Inadmissibility of the usual estimator for mean of multivariate normal distribution. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1: Contributions to the theory of statistics, 197–206. Berkeley, CA: University of California Press.
- Toutenburg, H., V. K. Srivastava, B. Schaffrin, and C. Heumann. 2003. Efficiency properties of weighted mixed regression estimation. Metron 1:91–103.
- Yang, H., and X. Chang. 2010. A new two-parameter estimator in linear regression. Communications in Statistics – Theory and Methods 39 (6):923–34. doi:10.1080/03610920902807911.
- Zhao, Y., A. H. Lee, and Y. V. Hui. 1994. Influence diagnostics for generalized linear measurement error models. Biometrics 50 (4):1117–28.