### A note on ridge regression modeling techniques

#### Abstract

#### References

Bradly, R. A., Srivastava, S. S. (1997). Correlation in polynomial regression. URL: http://stat.fsu.edu/techreports/M409.pdf

Cannon, A. J. (2009). Negative ridge regression parameters for improving the covariance structure of multivariate linear downscaling models. Int. J. Climatol., 29, 761-769.

Chatterjee, S., Hadi, A. S. (2006). Regression Analysis by Example. John Wiley & Sons, Inc., Hoboken, New Jersey.

Dorugade, A. V., Kashid, D. N. (2010). Alternative method for choosing ridge parameter for regression. Applied Mathematical Science, 4(9), 447-456.

El-Dereny, M. and Rashwan, N. I. (2011). Solving Multicollinearity Problem Using Ridge Regression Models. Int. J. Contemp. Math. Sciences, 6(12), 585-600.

Faraway, J. J. (2002). Practical regression and ANOVA using R. http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf

Fearn, T. (1993). A misuse of ridge regression in the calibration of a near infrared reflectance instrument. Applied Statistics, 32, 73-79.

Hoerl, A. E., Kennard, R. W., Hoerl, R. W.(1985). Practical use of ridge regression: A challenge met. Applied Statistics, 34(2), 114-120.

Hoerl, A.E., Kennard, R.W., Baldwin, K.F. (1975). Ridge regression: Some simulations.Communications in Statistics, 4, 105-123.

Hoerl, A. E., Kennard, R.W.(1970a). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.

Hoerl, A. E., Kennard, R. W. (1970b). Ridge regression: Applications to nonorthogonal problems. Technometrics, 12, 69-82, 1970b.

Khalaf, G., Shukur, G. (2005). Choosing ridge parameter for regression problem. Communications in Statistics–Theory and Methods, 34, 1177-1182.

Kibria, B. M. (2003). Performance of some ridge regression estimators. Communication in Statistics – Simulation and Computation, 32, 419-435.

Lawless, J. F., Wang, P. A. (1976). Simulation study of ridge and other regression estimators. Communications in Statistics –Theory and Methods, 14, 1589-1604.

Longley, J. W.(1976). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

Lin, L., Kmenta, J. (1982). Ridge Regression under Alternative Loss Criteria. The Review of Economics and Statistics, 64(3), 488-494.

Malinvaud, E. (1968). Statistical Methods of Econometrics, Rand-McNally, Chicago.

Mardikyan, S., Cetin, E.(2008). Efficient Choice of Biasing Constant for Ridge Regression. Int. J. Contemp. Math. Sciences, 3, 527-547.

Marquardt, D. W., Snee, R. D. (1975). Ridge regression in practice. The American Statistician, 29(1), 3-20.

Muniz, G., Kibria, B. M.(2009). On Some Ridge Regression estimator: An empirical comparison. Communication in Statistics–Simulation and Computation, 38, 62-630.

Myers, R. H.(1986). Classical and Modern Regression with Applications. PWS-KENT Publishing Company, Massachusetts.

Sparks, R. (2004). SUR Models Applied To an Environmental Situation with Missing Data and Censored Values. Journal of Applied Mathematics and Decision Sciences, 8(1), 15-32, 2004.

Wethril, H. (1986). Evaluation of ordinary Ridge Regression. Bulletin of Mathematical Statistics, 18, 1-35, 1986.

Yahya, W.B., Adebayo, S.B., Jolayemi, E.T., Oyejola, B.A., Sanni, O.O.M. (2008). Effects of non-orthogonality on the efficiency of seemingly unrelated regression (SUR) models. InterStat Journals, 1-29.

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