829
Views
32
CrossRef citations to date
0
Altmetric
Article

Beta ridge regression estimators: simulation and application

ORCID Icon &
Pages 4280-4292 | Received 28 Jul 2020, Accepted 21 Jul 2021, Published online: 06 Aug 2021

References

  • Abonazel, M. R. 2018. A practical guide for creating Monte-Carlo simulation studies using R. International Journal of Mathematics and Computational Science 4 (1):18–33.
  • Abonazel, M. R. 2019. New ridge estimators of SUR model when the errors are serially correlated. International Journal of Mathematical Archive 10 (7):53–62.
  • Abonazel, M. R., and R. A. Farghali. 2019. Liu-type multinomial logistic estimator. Sankhya B 81 (2):203–25. doi:10.1007/s13571-018-0171-4.
  • Aktaş, S., and H. Unlu. 2017. Beta regression for the indicator values of well-being index for provinces in Turkey. Journal of Engineering Technology and Applied Sciences 2 (2):101–11. doi:10.30931/jetas.321165.
  • Algamal, Z. Y. 2019. Performance of ridge estimator in inverse Gaussian regression model. Communications in Statistics-Theory and Methods 48 (15):3836–49.
  • 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.
  • Alkhamisi, M., G. Khalaf, and G. Shukur. 2006. Some modifications for choosing ridge parameters. Communications in Statistics - Theory and Methods 35 (11):2005–20. doi:10.1080/03610920600762905.
  • Amin, M., M. Qasim, M. Amanullah, and S. Afzal. 2020. Performance of some ridge estimators for the gamma regression model. Statistical Papers 61 (3):997–1026. doi:10.1007/s00362-017-0971-z.
  • Asar, Y., and M. Erişoğlu. 2021. Influence diagnostics in two-parameter ridge regression. Journal of Data Science 14 (1):33–52. doi:10.6339/JDS.201601_14(1).0003.
  • Bayer, F. M., and F. Cribari-Neto. 2017. Model selection criteria in beta regression with varying dispersion. Communications in Statistics - Simulation and Computation 46 (1):729–46. doi:10.1080/03610918.2014.977918.
  • Bhat, S. S. 2016. A comparative study on the performance of new ridge estimators. Pakistan Journal of Statistics and Operation Research 12 (2):317–25. doi:10.18187/pjsor.v12i2.1188.
  • Cribari-Neto, F., and A. Zeileis. 2010. Beta Regression in R. Journal of Statistical Software 34 (2):1–24. doi:10.18637/jss.v034.i02.
  • Dorugade, A. V. 2014. New ridge parameters for ridge regression. Journal of the Association of Arab Universities for Basic and Applied Sciences 15 (1):94–9. doi:10.1016/j.jaubas.2013.03.005.
  • Dorugade, A. V., and D. N. Kashid. 2010. Alternative method for choosing ridge parameter for regression. Applied Mathematical Sciences 4 (9):447–56.
  • Elgohary, M. M., M. R. Abonazel, N. M. Helmy, and A. R. Azazy. 2019. New robust-ridge estimators for partially linear model. International Journal of Applied Mathematical Research 8 (2):46–52. doi:10.14419/ijamr.v8i2.29932.
  • Espinheira, P. L., L. C. M. da Silva, and A. D. O. Silva. 2015. Prediction measures in beta regression models. arXiv preprint arXiv:1501.04830.
  • Espinheira, P. L., L. C. M. da Silva, A. D. O. Silva, and R. Ospina. 2019. Model selection criteria on beta regression for machine learning. Machine Learning and Knowledge Extraction 1 (1):427–49. doi:10.3390/make1010026.
  • Espinheira, P. L., S. L. Ferrari, and F. Cribari-Neto. 2008. On beta regression residuals. Journal of Applied Statistics 35 (4):407–19. doi:10.1080/02664760701834931.
  • Farghali, R. A., M. Qasim, B. G. Kibria, and M. R. Abonazel. 2021. Generalized two-parameter estimators in the multinomial logit regression model: Methods, simulation and application. Communications in Statistics-Simulation and Computation. Advance online publication. doi:10.1080/03610918.2021.1934023.
  • Ferrari, S., and F. Cribari-Neto. 2004. Beta regression for modelling rates and proportions. Journal of Applied Statistics 31 (7):799–815. doi:10.1080/0266476042000214501.
  • Frisch, R. 1934. Statistical confluence analysis by means of complete regression systems. University Institute of Economics. Cambridge University Press.
  • Göktaş, A., and V. Sevinc. 2016. Two new ridge parameters and a guide for selecting an appropriate ridge parameter in linear regression. Gazi University Journal of Science 29 (1):201–11.
  • Gruber, M. H. J. 1998. Improving Efficiency by Shrinkage: The James-Stein and Ridge Regression Estimators (1st ed.). New York: CRC Press. doi:10.1201/9780203751220.
  • Gunst, R. F., and R. L. Mason. 1977. Biased estimation in regression: An evaluation using mean squared error. Journal of the American Statistical Association 72 (359):616–28. doi:10.1080/01621459.1977.10480625.
  • Hocking, R. R., F. M. Speed, and M. J. Lynn. 1976. A class of biased estimators in linear regression. Technometrics 18 (4):425–37. doi:10.1080/00401706.1976.10489474.
  • Hoerl, A. E., and R. W. Kennard. 1970a. Ridge regression: Biased estimation for non-orthogonal problems. Technometrics 12 (1):55–67. doi:10.1080/00401706.1970.10488634.
  • Hoerl, A. E., and R. W. Kennard. 1970b. Ridge regression: Applications to non-orthogonal problems. Technometrics 12 (1):69–82. doi:10.1080/00401706.1970.10488635.
  • Hoerl, A. E., R. W. Kennard, and K. F. Baldwin. 1975. Ridge regression: Some simulations. Communications in Statistics-Theory and Methods 4 (2):105–23.
  • Kibria, B. G. 2003. Performance of some new ridge regression estimators. Communications in Statistics - Simulation and Computation 32 (2):419–35. doi:10.1081/SAC-120017499.
  • Kibria, B. G., K. Månsson, and G. Shukur. 2013. Some ridge regression estimators for the zero-inflated Poisson model. Journal of Applied Statistics 40 (4):721–35. doi:10.1080/02664763.2012.752448.
  • Lukman, A. F., K. Ayinde, B. G. Kibria, and E. T. Adewuyi. 2020. Modified ridge-type estimator for the gamma regression model. Communications in Statistics-Simulation and Computation :1–15. doi:10.1080/03610918.2020.1752720.
  • Lukman, A. F., B. Aladeitan, K. Ayinde, and M. R. Abonazel. 2021. Modified ridge-type for the Poisson regression model: Simulation and application. Journal of Applied Statistics. Advance online publication. doi:10.1080/02664763.2021.1889998.
  • Månsson, K. 2012. On ridge estimators for the negative binomial regression model. Economic Modelling 29 (2):178–84. doi:10.1016/j.econmod.2011.09.009.
  • Månsson, K., and G. Shukur. 2011. A Poisson ridge regression estimator. Economic Modelling 28 (4):1475–81. doi:10.1016/j.econmod.2011.02.030.
  • Nordberg, L. 1982. A procedure for determination of a good ridge parameter in linear regression. Communications in Statistics - Simulation and Computation 11 (3):285–309. doi:10.1080/03610918208812264.
  • Norouzirad, M., and M. Arashi. 2019. Preliminary test and Stein-type shrinkage ridge estimators in robust regression. Statistical Papers 60 (6):1849–82. doi:10.1007/s00362-017-0899-3.
  • Pasha, G. R., and M. A. Shah. 2004. Application of ridge regression to multicollinear data. Journal of Research (Science) 15 (1):97–106.
  • Rady, E. A., M. R. Abonazel, and I. M. Taha. 2018. Ridge estimators for the negative binomial regression model with application. The 53rd Annual Conference on Statistics, Computer Science, and Operation Research 3-5 Dec, 2018. ISSR, Cairo University.
  • Rady, E. A., M. R. Abonazel, and I. M. Taha. 2019a. A new biased estimator for zero-inflated count regression models. Journal of Modern Applied Statistical Methods Accepted paper (July 2019).
  • Rady, E. A., M. R. Abonazel, and I. M. Taha. 2019b. New shrinkage parameters for Liu-type zero inflated negative binomial estimator. The 54th Annual Conference on Statistics, Computer Science, and Operation Research 3-5 Dec, 2019. FGSSR, Cairo University.
  • Saleh, A. M. E., M. Arashi, and B. G. Kibria. 2019. Theory of Ridge Regression Estimation with Applications (Vol. 285). John Wiley & Sons.
  • Schaefer, R. L., L. D. Roi, and R. A. Wolfe. 1984. A ridge logistic estimator. Communications in Statistics - Theory and Methods 13 (1):99–113. doi:10.1080/03610928408828664.
  • Simas, A. B., W. Barreto-Souza, and A. V. Rocha. 2010. Improved estimators for a general class of beta regression models. Computational Statistics & Data Analysis 54 (2):348–66. doi:10.1016/j.csda.2009.08.017.
  • Smithson, M., and J. Verkuilen. 2006. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods 11 (1):54–71. doi:10.1037/1082-989X.11.1.54.
  • Zeebari, Z., G. Shukur, and B. M. G. Kibria. 2012. Modified ridge parameters for seemingly unrelated regression model. Communications in Statistics - Theory and Methods 41 (9):1675–91. doi:10.1080/03610926.2010.549281.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.