Two-parameter ridge estimation in seemingly unrelated regression models

References

• Alkhamisi, M. A., and G. Shukur. 2008. Developing ridge parameters for sur model. Communications in Statistics - Theory and Methods 37 (4):544–64. doi:10.1080/03610920701469152.
   Web of Science ®Google Scholar
• Babu, S. K., M. Ramanaiah, and V. S. Kumar. 2013. Adaptable ridge regression estimation for sure model. Applied Mathematical Sciences 7 (143):7137–41. doi:10.12988/ams.2013.311624.
   Google Scholar
• Binkley, J. K. 1982. The effect of variable correlation on the efficiency of seemingly unrelated regression in a two-equation model. Journal of the American Statistical Association 77 (380):890–5. doi:10.1080/01621459.1982.10477903.
   Web of Science ®Google Scholar
• Brouns, R., and P. De Deyn. 2004. Neurological complications in renal failure: A review. Clinical Neurology and Neurosurgery 107 (1):1–16. doi:10.1016/j.clineuro.2004.07.012.
   PubMed Web of Science ®Google Scholar
• Burn, D., and D. Bates. 1998. Neurology and the kidney. Journal of Neurology, Neurosurgery & Psychiatry 65 (6):810–21. doi:10.1136/jnnp.65.6.810.
   PubMed Web of Science ®Google Scholar
• El-Salam, M. E.-F A. 2011. The efficiency of some alternative ridge estimators for seemingly unrelated regressions. Asian Journal of Mathematics and Statistics 4 (3):128–89.
   Google Scholar
• Firinguetti, L. 1997. Ridge regression in the context of a system of seemingly unrelated regression equations. Journal of Statistical Computation and Simulation 56 (2):145–62. doi:10.1080/00949659708811785.
   Web of Science ®Google Scholar
• Firinguetti, L., and H. Rubio. 2008. Some asymptotic properties of ridge regression in a system of seemingly unrelated regression equations. Communications in Statistics - Theory and Methods 37 (15):2433–46. doi:10.1080/03610920801931853.
   Web of Science ®Google Scholar
• Fraser, C. L., and A. I. Arieff. 1988. Nervous system complications in uremia. Annals of Internal Medicine 109 (2):143–53. doi:10.7326/0003-4819-109-2-143.
   PubMed Web of Science ®Google Scholar
• Hoerl, A. E., R. W. Kannard, and K. F. Baldwin. 1975. Ridge regression: Some simulations. Communications in Statistics 4 (2):105–23. doi:10.1080/03610927508827232.
   Web of Science ®Google Scholar
• Hoerl, A. E., and R. W. Kennard. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12 (1):55–67. doi:10.1080/00401706.1970.10488634.
   Web of Science ®Google Scholar
• Hoerl, A. E., and R. W. Kennard. 2000. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 42 (1):80–6. doi:10.1080/00401706.2000.10485983.
   Web of Science ®Google Scholar
• Kejian, L. 1993. A new class of blased estimate in linear regression. Communications in Statistics - Theory and Methods 22 (2):393–402. doi:10.1080/03610929308831027.
   Web of Science ®Google Scholar
• Lacerda, G., T. Krummel, and E. Hirsch. 2010. Neurologic presentations of renal diseases. Neurologic Clinics 28 (1):45–59. doi:10.1016/j.ncl.2009.09.003.
   PubMed Web of Science ®Google Scholar
• Lipovetsky, S. 2006. Two-parameter ridge regression and its convergence to the eventual pairwise model. Mathematical and Computer Modelling 44 (3-4):304–18. doi:10.1016/j.mcm.2006.01.017.
   Web of Science ®Google Scholar
• Lipovetsky, S., and W. M. Conklin. 2005. Ridge regression in two-parameter solution. Applied Stochastic Models in Business and Industry 21 (6):525–40. doi:10.1002/asmb.603.
   Web of Science ®Google Scholar
• Ma, T., and S. Wang. 2009. Improved estimates of regression coefficients in seemingly unrelated regression models with two equations. Chinese Journal of Applied Probability and Statistics 25 :15–32.
   Google Scholar
• Montgomery, D. C., E. A. Peck, and G. G. Vining. 2012. Introduction to linear regression analysis. New York: Wiley.
   Google Scholar
• Rana, S., and M. Al Amin. 2015. An alternative method of estimation of sur model. American Journal of Theoretical and Applied Statistics 4 (3):150. doi:10.11648/j.ajtas.20150403.20.
   Google Scholar
• Şİray, G. Ü., and S. Toker. 2013. Restricted two parameter ridge estimator. Australian & New Zealand Journal of Statistics 55 (4):455–69. doi:10.1111/anzs.12053.
   Web of Science ®Google Scholar
• Tobimatsu, S., and G. G. Celesia. 2006. Studies of human visual pathophysiology with visual evoked potentials. Clinical Neurophysiology 117 (7):1414–33. doi:10.1016/j.clinph.2006.01.004.
   PubMed Web of Science ®Google Scholar
• Toker, S., and S. Kaç Iranlar. 2013. On the performance of two parameter ridge estimator under the mean square error criterion. Applied Mathematics and Computation 219 (9):4718–28. doi:10.1016/j.amc.2012.10.088.
   Web of Science ®Google Scholar
• Vigneau, E., M. Devaux, E. Qannari, and P. Robert. 1997. Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration. Journal of Chemometrics 11 (3):239–49. doi:10.1002/(SICI)1099-128X(199705)11:3<239::AID-CEM470>3.0.CO;2-A.
   Web of Science ®Google Scholar
• Walsh, P., N. Kane, and S. Butler. 2005. The clinical role of evoked potentials. Journal of Neurology, Neurosurgery & Psychiatry 76 (suppl_2):ii16–ii22. doi:10.1136/jnnp.2005.068130.
   PubMedGoogle Scholar
• Wiener, C., A. Fauci, E. Braunwald, D. Kasper, S. Hauser, D. Longo, J. Jameson, J. Loscalzo, and C. Brown. 2012. Harrison’s principles of internal medicine self-assessment and board review. 18th ed. New York, NY: McGraw Hill Professional.
   Google Scholar
• Zellner, A. 1962. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 57 (298):348–68. doi:10.1080/01621459.1962.10480664.
   Web of Science ®Google Scholar