On the mixed Kibria-Lukman estimator for the linear regression model.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 07 2022
20 07 2022
Historique:
received:
03
04
2022
accepted:
13
07
2022
entrez:
20
7
2022
pubmed:
21
7
2022
medline:
23
7
2022
Statut:
epublish
Résumé
This paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria-Lukman estimator by combining the mixed estimator and the Kibria-Lukman estimator.This new estimator is a general estimation, including OLS estimator, mixed estimator and Kibria-Lukman estimator as special cases. In addition, we discuss the advantages of the new estimator based on MSEM criterion, and illustrate the theoretical results through examples and simulation analysis.
Identifiants
pubmed: 35859042
doi: 10.1038/s41598-022-16689-z
pii: 10.1038/s41598-022-16689-z
pmc: PMC9300599
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
12430Informations de copyright
© 2022. The Author(s).
Références
Massy, W. F. Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 60, 234–256 (1965).
doi: 10.1080/01621459.1965.10480787
Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
doi: 10.1080/00401706.1970.10488634
Swindel, B. F. Good estimators based on prior information. Commun. Stat. Theroy Methods 5, 1065–1075 (1976).
doi: 10.1080/03610927608827423
Lukman, A. F., Ayinde, K., Binuomote, S. & Onate, A. C. Modified ridge-type estimator to cambat multicollinearity. J. Chemom. e3125, 1–12 (2019).
Liu, K. J. A new class of biased estimate in linear regression. Commun. Stat. Theroy Methods 22, 393–402 (1993).
doi: 10.1080/03610929308831027
Akdeniz, F. & Kaciranlar, S. On the almost unbiased generalized Liu estimator and unbiased estimation of the bias and MSE. Commun. Stat. Theroy Methods 24, 1789–1797 (1995).
doi: 10.1080/03610929508831585
Liu, K. J. Using Liu-type estimator to combat collinearity. Commun. Stat. Theroy Methods 32, 1009–1020 (2003).
doi: 10.1081/STA-120019959
Baye, M. R. & Parker, D. F. Combining ridge and principal component regression: A money demand illustration. Commun. Stat. Theroy Methods 13, 197–225 (1984).
doi: 10.1080/03610928408828675
Kaciranlar, S. & Sakallioglu, S. Combining the Liu estimator and the principal component regression estimator. Commun. Stat. Theroy Methods 30, 2699–2705 (2001).
doi: 10.1081/STA-100108454
Ozkale, M. R. & Kaciranlar, S. The restricted and unrestricted two-parameter estimators. Commun. Stat. Theroy Methods 36, 2707–2725 (2007).
doi: 10.1080/03610920701386877
Batah, F. M., Ozkale, M. R. & Gore, S. D. Combining unbiased ridge and principal component regressions estimators. Commun. Stat. Theroy Methods 38, 2201–2209 (2009).
doi: 10.1080/03610920802503396
Yang, H. & Chang, X. F. A new two-parameter estimator in linear regression. Commun. Stat. Theroy Methods 39(6), 923–934 (2010).
doi: 10.1080/03610920902807911
Lukman, A. F., Ayinde, K., Oludoun, O. & Onate, C. A. Combining modified ridge-type and principal component regression estimators. Sci. Afr. e536, 1–8 (2020).
Kibria, B. M. G. & Lukman, A. F. A new ridge-type estimator for the linear regression model: Simulations and applications. Scientifica https://doi.org/10.1155/2020/9758378 (2020).
doi: 10.1155/2020/9758378
pubmed: 32399315
pmcid: 7204127
Theil, H. & Goldberger, A. S. On pure and mixed estimation in econometrics. Int. Econ. Rev. 2, 65–78 (1961).
doi: 10.2307/2525589
Theil, H. On the use of incomplete prior information in regression analysis. J. Am. Stat. Assoc. 58, 401–414 (1963).
doi: 10.1080/01621459.1963.10500854
Schiffrin, B. & Toutenburg, H. Weighted mixed regression. Z. Angew. Math. Mech. 70, 735–738 (1990).
Hubert, M. H. & Wijekoon, P. Improvement of the Liu estimator in linear regression coefficient. Stat. Pap. 47, 471–479 (2006).
doi: 10.1007/s00362-006-0300-4
Yang, H. & Xu, J. W. An alternative stochastic restricted Liu estimator in linear regression model. Stat. Pap. 50, 369–647 (2009).
doi: 10.1007/s00362-007-0102-3
Ozbay, N. & Kaciranlar, K. S. Estimation in a linear regression model with stochastic linear restrictions: A new two-parameter-weighted mixed estimator. J. Stat. Comput. Simul. 88, 1669–1683 (2018).
doi: 10.1080/00949655.2018.1442836
Gruber, M. H. J. Improving Efficiency by Shrinkage: The James–Stein and Ridge Regression estimators (Marcel Dekker Inc, 1998).
Akdeniz, F. & Erol, H. Mean Squared error matrix comparisons of some biased estimator in linear regression. Commun. Stat. Theroy Methods 32(12), 2389–2413 (2003).
doi: 10.1081/STA-120025385
Arashi, M. et al. Ridge regression and its applications in genetic studies. PLoS One 16(4), e0245376 (2021).
doi: 10.1371/journal.pone.0245376
Roozbeh, M. & Azen, S. P. Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion. Comput. Stat. Data Anal. 117, 45–61 (2018).
doi: 10.1016/j.csda.2017.08.002
Roozbeh, M., Arahi, M. & Hamzah, N. A. Generalized cross-validation for simultaneous optimization of tuning parameters in ridge regression. Iran. J. Sci. Technol. Trans. A, Sci. 44(2), 473–485 (2020).
doi: 10.1007/s40995-020-00851-1
Roozbeh, M., Hesamianb, G. & Akbaric, M. G. Ridge estimation in semi-parametric regression models under the stochastic restriction and correlated elliptically contoured errors. J. Comput. Appl. Math. 378, 112940 (2020).
doi: 10.1016/j.cam.2020.112940
Roozbeh, M. & Hamzah, N. A. Uncertain stochastic ridge estimation in partially linear regression models with elliptically distributed errors. Statistics 3, 494–523 (2022).
McDonald, M. C. & Galarneau, D. I. A Monte Carlo evaluation of ridge-type estimators. J. Am. Stat. Assoc. 70, 407–416 (1975).
doi: 10.1080/01621459.1975.10479882
Gibbons, D. G. A simulation study of some ridge estimators. J. Am. Stat. Assoc. 76, 131–139 (1981).
doi: 10.1080/01621459.1981.10477619