1. Hoerl A.E. and Kennard R.W. Ridge regression: biased estimation for non-orthogonal problems. Technometrics, 12: 69–82 (1970).
2. Belsley D.A., Kuh E. and Welsch R.E. Regression diagnostics: identifying data and sources of collinearity. John Wiley & Sons, New York, (2004).
3. Rao C.R., Toutenburg H., Shalabh and Heumann C. Linear models and generalizations, Least squares and alternatives. Springer, Berlin, (2008).
4. Sarkar N. A new estimator combining the ridge regression and the restricted least squares methods of estimation. Commun. Stat.-Theor M, 21: 1987–2000 (1992).
5. Özkale M.R. A stochastic restricted ridge regression estimator. J. Multivar Anal., 100: 1706–1716 (2009).
6. Bayhan G.M. and Bayhan M. Forcasting using autocorrelated errors and multicollinear predictor variables. Comp. ind. Eng., 34(2): 413-421 (1998).
7. Alkhamisi M.A. Ridge estimation in linear models with autocorrelated errors. Commun. Stat.-Theor M, 39: 2630–2644 (2010).
8. Groβ J. Restricted ridge estimation. Stat. Probabil. Lett., 65: 57-64 (2003).
9. Alheety M.I. and Golam Kibria B.M. A Generalized stochastic restricted ridge regression estimator. Commun. Stat.-Theor M, 43: 4415–4427 (2014).
10. Chatterjee S. and Hadi A.S. Influential observations, high leverage points, and outliers in linear regression. Stat. Science, 1(3): 379–416 (1986).
11. Roy S.S. and Guria S. Regression diagnostics in an autocorrelated model. Braz. J. Probab. Statist., 18: 103–112 (2004).
12. Özkale M.R. and Acar T.S. Leverages and influential observations in a regression model with autocorrelated errors. Commun. Stat.-Theor M, 44: 2267–2290 (2015).
13. Acar T.S and Özkale M.R. Influence measures in ridge regression when the error terms follow an AR(1) process. Comput. Stat., 31(3): 879–898 (2016).
14. Ghapani F., Rasekh A.R., Akhoond M.R. and Babadi B. Detection of outliers and influential observations in linear ridge measurement error models with stochastic linear restrictions. J. Sci. I. R. Iran, 26(4): 355 - 366 (2015).
15. Wang J. Statistical diagnosis of linear regression model with the random constraints and Bayes method. Nanjing University of Science and Technology, Nanjing, (2007).
16. Wen H.W. and Wing K.F. The mean-shift outlier model in general weighted regression and its applications. Comp. Stat. data an., 33(4): 429-441 (1999).
17. Pan J. and Xiong H. Outliers and influential observations in a ridge mean shift regression, System science and Mathematical science, 9(1): 12-26 (1995).
18. Ghapani F., Rasekh A.R. and Babadi B. Mean shift and influence measures in linear measurement error models with stochastic linear restrictions. Comm. Stat-Simulat.C., 46(6): 4499-4512 (2017).
19. Theil H. and Goldberger A.S. On pure and mixed statistical estimation in economics. Int. Econ. Rev., 2 : 65-78 (1961).
20 Troskie C.G., Chalton D.O., Stewart T.J. and Jacobs M. Detection of outliers and influential observations in regression analysis using stochastic prior information, Commun. Stat.-Theor M, 23(12): 3453–3476 (1994).
21. Firinguetti L. A simulation study of ridge regression estimators with autocorrelated errors. Commun. Stat. Simulat., 18(2): 673-701 (1989).
22. Judge G.C., Hill R.C., Griffiths W.E., Lütkepohl H. and Lee T.C. Introduction to the Theory and Practice of Econometrics. John Wiley & Sons, New York, (1988).
23. Seber G.A.F. and Lee A. Linear Regression Analysis. John Wiley & Sons, New Jersey, (2003).
24. Montgomery D.C., Peck E.A., and Vining G.G. Linear Regression Analysis,5th Ed., John Wiley & Sons, New Jersey, (2012).
26. McDonald G.C. and Galarneau D.I. A monte carlo evaluation of some ridge type estimators. J. Am. Stat. Assoc., 70: 407–416 (1975).
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