Volume 4 • Issue 2 • PP: 01–12 • 2024
Proposed Two-Parameter Estimator for Estimating Linear Regression Model and Comparing It with Some Other Estimators
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
In this paper, a new two-parameter estimator was proposed to estimate the parameters of the linear regression model that has the ability to face the problem of Multicollinearity based on the previous information about the parameters to be estimated and this estimator was compared with the two-parameter estimator of the linear regression model of Kaciranlar and the two-parameter estimator of the linear regression model (Lokman et al. [1]) using the mean square error criterion (MSE) for each model by conducting Monte-Carlo simulation to study the behavior of the proposed estimator. It was concluded that the proposed method is better than the rest of the estimation methods because it achieved the lowest comparison criteria, and in the case of high Multicollinearity between the explanatory variables, the proposed method was very effective in solving this problem. Data representing (100) observations of the number of women with Irritable Bowel Syndrome (IBS) for the years (2020-2023) from the Karbala Holy Health Department were used, which represents the dependent variable (y) and a group of variables affecting the incidence of the disease, with nineteen variables. It was concluded that irritable bowel syndrome among women is decreasing, as the predictive values according to the proposed method are appropriate for the estimated values during the next five years.
Keywords
References
[1] L. Dunai, J. Martins, K. Umetani, O. Lucia, Y. Ibrahim, and G. K. Appuhamillage, “E-learning in industrial electronics during COVID-19,” in 2021 22nd IEEE International Conference on Industrial Technology (ICIT), 2021.
[2] T. B. L. and P. S. S., “An analysis on e-learning and its recommendations,” Advances in Data Mining and Database Management, pp. 166–187, 2021.
[3] M. Habes, S. Ali, A. Khalid, H. A. Haykal, M. Elareshi, T. Khan, et al., “E-learning acceptance during the COVID-19 outbreak: A cross-sectional study,” Lecture Notes in Networks and Systems, pp. 65–77, 2021.
[4] R. F. K. Basha and K. Venusamy, “A detailed study of effective online technical teaching aids for augmented learning in COVID-19 pandemic,” in 2020 Sixth International Conference on E-Learning (Econf), 2020.
[5] H. T. T. Dung and T. Q. Hai, “Changes in students’ experiences and perceptions towards e-learning at Hoa Sen University during COVID-19 pandemic,” AsiaCALL Online Journal, vol. 13, pp. 22–39, 2021.
[6] K. Shinghal, A. Saxena, N. Saxena, and R. Misra, “IoT based modified hybrid blended learning model for education,” in 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM), 2020.
[7] I. Popchev, D. Orozova, and S. Stoyanov, “IoT and big data analytics in e-learning,” in 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2019.
[8] C. M. Mohammed and S. Askar, “Machine learning for IoT healthcare applications: A review,” International Journal of Science and Business, vol. 5, pp. 42–51, 2021.
[9] G. Ilieva and T. Yankova, “IoT in distance learning during the COVID-19 pandemic,” TEM Journal, pp. 1669–1674, 2020.
[10] M. Lamri, S. Akrouf, A. Boubetra, A. Merabet, L. Selmani, and D. Boubetra, “From local teaching to distant teaching through IoT interoperability,” in 2014 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL2014), 2014.
[11] A. A. Rukmana and B. Mulyanti, “Internet of Things (IoT): Web learning for smart school system,” IOP Conference Series: Materials Science and Engineering, vol. 830, art. 032042, 2020.
[12] R. Lea and M. Blackstock, “City Hub: A cloud-based IoT platform for smart cities,” in 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 2014.
[13] S. Shamshoddin, J. Khader, and S. Gani, “Predicting consumer preferences in electronic markets based on IoT and social networks using deep-learning-based collaborative filtering techniques,” Electronic Commerce Research, vol. 20, pp. 241– 258, 2020.
[14] A. M. Rahmani, R. A. Naqvi, M. H. Malik, T. S. Malik, M. Sadrishojaei, M. Hosseinzadeh, et al., “E-learning development based on Internet of Things and blockchain technology during COVID-19 pandemic,” Mathematics, vol. 9, art. 3151, 2021.
[15] J. Kim, J. Yun, S.-C. Choi, D. N. Seed, G. Lu, M. Bauer, et al., “Standard-based IoT platforms interworking: Implementation, experiences, and lessons learned,” IEEE Communications Magazine, vol. 54, pp. 48–54, 2016.
[16] M. Bayani, K. Leiton, and M. Loaiza, “Internet of Things(IoT) advantages on e-learning in smart cities,” International Journal of Development Research, vol. 7, pp. 17747–17753, 2017.
[17] K. Siripongdee, P. Pimdee, and S. Tuntiwongwanich, “A blended learning model with IoT-based technology: Effectively used when the COVID-19 pandemic?,” Journal for the Education of Gifted Young Scientists, vol. 8, pp. 905–917, 2020.
[18] N. A. Jasim, H. T. Salim AlRikabi, and M. S. Farhan, “Internet of Things (IoT) application in the assessment of learning process,” IOP Conference Series: Materials Science and Engineering, vol. 1184, art. 012002, 2021.
[19] D. Pandey, N. Singh, V. Singh, and M. W. Khan, “Paradigms of smart education with IoT approach,” EAI/Springer Innovations in Communication and Computing, pp. 223–233, 2021.
[20] A. T. Owolabi, K. Ayinde, J. I. Idowu, O. J. Oladapo, and A. F. Lukman, “A new two-parameter estimator in the linear regression model with correlated regressors,” Journal of Statistics Applications & Probability, vol. 11, no. 2, pp. 499–512, 2022.
[21] M. N. Akram, M. Amin, F. Sami, A. B. Mastor, O. M. Egeh, A. H. Muse, and G. Psarrakos, “A new Conway–Maxwell– Poisson Liu regression estimator—method and application,” Journal of Mathematics, vol. 2022, pp. 1–12, 2022.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.