References
- Akdeniz, F., and S. Kaçiranlar. 2001. More on the new biased estimator in linear regression. Sankhyā: The Indian Journal of Statistics 63 (3):321–25.
- Akram, M. N., M. Amin, and M. Amanullah. 2020. Two-parameter estimator for the inverse Gaussian regression model. Communications in Statistics - Simulation and Computation. Advance online publication. doi:10.1080/03610918.2020.1797797.
- Akram, M. N., M. Amin, and M. Amanullah. 2021. James Stein estimator for the inverse Gaussian regression model. Iranian Journal of Science and Technology, Transactions A: Science 45 (4):1389–403. doi:10.1007/s40995-021-01133-0.
- Akram, M. N., M. Amin, and M. Qasim. 2020. A new Liu-type estimator for the Inverse Gaussian Regression Model. Journal of Statistical Computation and Simulation 90 (7):1153–72. doi:10.1080/00949655.2020.1718150.
- Algamal, Z. Y. 2018a. Developing a ridge estimator for the gamma regression model. Journal of Chemometrics 32 (10):e3054-12. doi:10.1002/cem.3054.
- Algamal, Z. Y. 2018b. Shrinkage estimators for gamma regression model. Electronic Journal of Applied Statistical Analysis 11 (1):253–68.
- Algamal, Z. Y. 2019. Performance of ridge estimator in inverse Gaussian regression model. Communication in Statistics - Theory and Methods 48 (15):3836–49.
- Alheety, M. I., and B. M. G. Kibria. 2009. On the Liu and almost unbiased Liu estimators in the presence of multicollinearity with heteroscedastic or correlated errors. Surveys in Mathematics and Applications 4:155–67.
- Amin, M., M. Amanullah, and M. Qasim. 2020. Diagnostic techniques for the inverse Gaussian regression model. Communications in Statistics - Theory and Methods. Advance online publication. doi:10.1080/03610926.2020.1777308.
- Amin, M., M. A. Ullah, and M. Aslam. 2016. Empirical evaluation of the inverse Gaussian regression residuals for the assessment of influential points. Journal of Chemometrics 30 (7):394–404. doi:10.1002/cem.2805.
- Amin, M., M. Qasim, S. Afzal, and K. Naveed. 2020. New ridge estimators in the inverse Gaussian regression: Monte Carlo simulation and application to chemical data. Communications in Statistics - Simulation and Computation. Advance online publication. doi:10.1080/03610918.2020.1797794.
- Amin, M., M. Qasim, M. A. Ullah, and S. Afzal. 2020. On the performance of some ridge estimators in gamma regression. Statistical Papers 61 (3):997–1026. doi:10.1007/s00362-017-0971-z.
- Amin, M., M. N. Akram, and Q. Ramzan. 2020. Bayesian estimation of ridge parameter under different loss functions. Communications in Statistics - Theory and Methods. Advance online publication. doi:10.1080/03610926.2020.1809675.
- Amin, M., M. N. Akram, and A. Majid. 2021. On the estimation of Bell regression model using ridge estimator. Communications in Statistics - Simulation and Computation. Advance online publication. doi:10.1080/03610918.2020.1870694.
- Amin, M., M. Faisal, and M. N. Akram. 2021. Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics. Journal of Chemometrics 35 (6):1–20. doi:10.1002/cem.3342.
- Amin, M., M. N. Akram, and B. M. G. Kibria. 2021. A new adjusted Liu estimator for the Poisson regression model. Concurrency and computation: Practice and experience. Advance online publication. doi:10.1002/cpe.6340.
- Dunder, E., S. Gumustekin, and G. Z. Cengiz. 2018. Variable selection in gamma regression models via artificial bee colony algorithm. Journal of Applied Statistics 45 (1):8–16. doi:10.1080/02664763.2016.1254730.
- Farebrother, R. W. 1976. Further results on the mean square error of ridge regression. Journal of the Royal Statistical Society: Series B (Methodological) 38 (3):248–50. doi:10.1111/j.2517-6161.1976.tb01588.x.
- 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.
- Khalaf, G., K. Månsson, P. Sjölander, and G. Shukur. 2014. A Tobit ridge regression. Communications in Statistics - Theory and Methods 43 (1):131–40. doi:10.1080/03610926.2012.655881.
- Kibria, B. M. 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. M. G. 2012. Some Liu and ridge-type estimators and their properties under the ill-conditioned Gaussian linear regression model. Journal of Statistical Computation and Simulation 82 (1):1–17. doi:10.1080/00949655.2010.519705.
- Kibria, B. M. G., and S. Banik. 2016. Some ridge regression estimators and their performances. Journal of Modern Applied Statistical Methods 15 (1):206–38. doi:10.22237/jmasm/1462075860.
- Kibria, B. M. G., and A. F. Lukman. 2020. A new ridge-type estimator for the linear regression model: Simulations and applications. Scientifica 2020:9758378. doi:10.1155/2020/9758378.
- Kibria, B. M. G., K. Månsson, and G. Shukur. 2012. Performance of some logistic ridge regression estimators. Computational Economics 40 (4):401–14. doi:10.1007/s10614-011-9275-x.
- Kinat, S., M. Amin, and T. Mahmood. 2020. GLM-based control charts for the inverse Gaussian response variable. Quality and Reliability Engineering International 36 (2):765–83. doi:10.1002/qre.2603.
- Kurtoğlu, F., and M. R. Özkale. 2016. Liu estimation in generalized linear models: Application on gamma distributed response variable. Statistical Papers 57 (4):911–28. doi:10.1007/s00362-016-0814-3.
- Li, Y., and H. Yang. 2012. A new Liu-type estimator in linear regression model. Statistical Papers 53 (2):427–37. doi:10.1007/s00362-010-0349-y.
- Liu, K. 1993. A new class of biased estimate in linear regression. Communication in Statistics - Theory Methods 22 (2):393–402.
- 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.
- Lukman, A. F., Z. Y. Algamal, B. M. G. Kibria, and K. Ayinde. 2021. The KL estimator for the inverse gaussian regression model. Concurrency and Computation: Practice and Experience 33 (13). doi:10.1002/cpe.6222.
- Lukman, A. F., and K. Ayinde. 2017. Review and classifications of the ridge parameter estimation techniques. Hacettepe Journal of Mathematics and Statistics 46 (5):953–67.
- Lukman, A. F., K. Ayinde, S. Binuomote, and O. A. Clement. 2019. Modified ridge‐type estimator to combat multicollinearity: Application to chemical data. Journal of Chemometrics 33 (5):e3125. doi:10.1002/cem.3125.
- Lukman, A. F., A. Emmanuel, O. A. Clement, and K. Ayinde. 2020. A modified ridge-type logistic estimator. Iranian Journal of Science and Technology, Transactions A: Science 44 (2):437–43. doi:10.1007/s40995-020-00845-z.
- 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. 2013. Developing a Liu estimator for the negative binomial regression model: Method and application. Journal of Statistical Computation and Simulation 83 (9):1773–80. doi:10.1080/00949655.2012.673127.
- Månsson, K., B. M. G. Kibria, and G. Shukur. 2012. On Liu estimators for the logit regression model. Economic Modelling 29 (4):1483–88. doi:10.1016/j.econmod.2011.11.015.
- 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.
- Naveed, K., M. Amin, S. Afzal, and M. Qasim. 2020. New shrinkage parameters for the inverse Gaussian Liu regression. Communications in Statistics-Theory and Methods. Advance online publication. doi:10.1080/03610926.2020.1791339.
- Özkale, M. R. 2019. The r–d class estimator in generalized linear models: Applications on gamma, Poisson and binomial distributed responses. Journal of Statistical Computation and Simulation 89 (4):615–40. doi:10.1080/00949655.2018.1563791.
- Özkale, M. R., and E. Arican. 2016. A new biased estimator in logistic regression model. Statistics 50 (2):1–253. doi:10.1080/02331888.2015.1123711.
- Qasim, M., M. Amin, and M. Amanullah. 2018. On the performance of some new Liu parameters for the gamma regression model. Journal of Statistical Computation and Simulation 88 (16):3065–80. doi:10.1080/00949655.2018.1498502.
- Qasim, M., M. Amin, and T. Omer. 2020. Performance of some new Liu parameters for the linear regression model. Communications in Statistics - Theory and Methods 49 (17):4178–96. doi:10.1080/03610926.2019.1595654.
- Qasim, M., B. M. G. Kibria, K. Månsson, and P. Sjölander. 2020. A new Poisson Liu regression estimator: Method and application. Journal of Applied Statistics 47 (12):2258–71. doi:10.1080/02664763.2019.1707485.
- Ramzan, Q., M. N. Akram, and M. Amin. 2021. Ridge parameter estimation for the linear regression model under different loss functions using T-K approximation. Communication in Statistics - Simulation and Computation. Advance online publication. doi:10.1080/03610918.2021.1962345.
- 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.
- Segerstedt, B. 1992. On ordinary ridge regression in generalized linear models. Communications in Statistics - Theory and Methods 21 (8):2227–46. doi:10.1080/03610929208830909.
- Trenkler, G., and H. Toutenburg. 1990. Mean squared error matrix comparisons between biased estimators - An overview of recent results. Statistical Papers 31 (1):165–79. doi:10.1007/BF02924687.
- Varathan, N., and P. Wijekoon. 2018. Liu-type logistic estimator under stochastic linear restrictions. Ceylon Journal of Science 47 (1):21–34. doi:10.4038/cjs.v47i1.7483.