پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 671 |
| تعداد مقالات | 9,781 |
| تعداد مشاهده مقاله | 69,568,675 |
| تعداد دریافت فایل اصل مقاله | 48,708,934 |
Atan regularized for the high dimensional Poisson regression model | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 2197-2202 اصل مقاله (857.22 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.6092 | ||
| نویسندگان | ||
| Ali Hameed Yousif* 1؛ Ahlam Hanash Gatea2 | ||
| 1College of Administration and Economic, Wasit University, Iraq | ||
| 2College of Languages, University of Baghdad, Iraq | ||
| تاریخ دریافت: 10 مهر 1400، تاریخ بازنگری: 30 آبان 1400، تاریخ پذیرش: 17 آذر 1400 | ||
| چکیده | ||
| Variable selection in Poisson regression with high dimensional data has been widely used in recent years. we proposed in this paper using a penalty function that depends on a function named a penalty. An Atan estimator was compared with Lasso and adaptive lasso. A simulation and application show that an Atan estimator has the advantage in the estimation of coefficient and variables selection. | ||
| کلیدواژه‌ها | ||
| Poisson regression؛ Lasso؛ Adaptive Lasso؛ Atan | ||
| مراجع | ||
|
[1] A. Alfons, C. Croux and S. Gelper, Sparse least trimmed squares regression for analyzing high-dimensional large data sets, Ann. Appl. Stat. 7(1) (2013) 226–248. [2] Z.Y. Algamal, Diagnostic in Poisson regression models, Electron. J. App. Stat. Anal. 5(2) (2012) 178–186. [3] Z.Y. Algamal and M.H. Lee, Adjusted adaptive Lasso in high-dimensional Poisson regression model, Modern Appl. Sci. 9(4) (2015) 170–177. [4] C. Choosawat, O. Reangsephet, P. Srisuradetchai and S. Lisawadi, Performance comparison of penalized regression methods in Poisson regression under high-dimensional sparse data with multicollinearity, Thai. Statist. 18(3) (2020) 306–318. [5] G.N. Collins, R.J. Lee, G.B. McKelvie, A.C.N. Rogers and M. Hehir, Relationship between prostate specific antigen, prostate volume and age in the benign prostate, Br. J. Urology 71(4) (1993) 445–450. [6] J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Amer. Statist. Assoc. 96(456) (2001) 1348–1360. [7] R. Koenker and G. Bassett Jr., Regression quantiles, Econometrica: Journal of the Econometric Society, 46(1) (1978) 33–50. [8] K. M˚ansson and G. Shukur, A Poisson ridge regression estimator, Economic Modelling, 28(4) (2011) 1475–1481. [9] J. Mwikali, S. Mwalili and A. Wanjoya, Penalized Poisson regression model using elastic net and least absolute shrinkage and selection operator (Lasso) penality, Int. J. Data Sci. Anal. 5(5) (2019) 99–103. [10] M.Y. Park and T. Hastie, L1-regularization path algorithm for generalized linear models, J. R. Statist. Soc. B 69(4) (2007) 659–775. [11] F. Shahzad, F. Abid, A.J. Obaid, B.K. Rai, M. Ashraf and A.S. Abdulbaqi, Forward stepwise logistic regression approach for determinants of hepatitis B & C among Hiv/Aids patients, Int. J. Nonlinear Anal. Appl. 12(Special Issue) (2021) 1367–1396. [12] R. Tibshirani, Regression shrinkage and selection via the Lasso, J. R. Statist. Soc. B 58(1) (1996) 267–288. [13] Y. Wang and L. Zhu, Variable selection and parameter estimation with the Atan regularization method, J. Probab. Statist. 2016 (2016) 1–12. [14] A.H. Yousif, Proposing robust LAD-shrink set estimator for high dimensional regression model, Int. J. Agric. Stat. Sci. 17 (2021) 1229–1234. [15] A.H. Yousif and O.A. Ali, Proposing robust LAD-Atan penalty of regression model estimation for high dimensional data, Baghdad Sci. J. 17(2) (2020) 550–555. [16] A.H. Yousif and W.J. Housain, Atan regularized in quantile regression for high dimensional data, J. Phys. Conf. Ser. 1818 (2021) 012098. [17] C. Zaldivar, On the Performance of some Poisson Ridge Regression Estimators, Master of Science Thesis, Florida International University, 2018. [18] H. Zou, The adaptive Lasso and its oracle properties, J. Amer. Statist. Assoc. 101(476) (2006) 1418–1429. | ||
|
آمار تعداد مشاهده مقاله: 44,402 تعداد دریافت فایل اصل مقاله: 37,251 |
||