Zobrazeno 1 - 10
of 71
pro vyhledávání: '"KAYODE, Ayinde"'
Publikováno v:
Journal of Nigerian Society of Physical Sciences, Vol 6, Iss 4 (2024)
Efficiency estimation in production technology has been a concern in economics, with methodologies such as Stochastic Frontier Analysis (SFA) playing a key role in this area. SFA has been pivotal in evaluating the efficiency of entities by isolating
Externí odkaz:
https://doaj.org/article/96b27d24a1ce4d879f230f72af43a36b
Autor:
Segun L. Jegede, Adewale F. Lukman, Ohud A. Alqasem, Maysaa Elmahi Abd Elwahab, Kayode Ayinde, B.M. Golam Kibria, Hezekiah Adewinbi
Publikováno v:
Scientific African, Vol 25, Iss , Pp e02324- (2024)
The linear regression model is a widely used statistical tool that forms most modelling concepts' basis. The ordinary least square estimator is often adopted to estimate the model's parameters. The estimator is considered efficient when there are no
Externí odkaz:
https://doaj.org/article/9f02682926034bb188ed24f6abcfda2c
Autor:
Joel, Adejumo Taiwo1 tjadejumo@lautech.edu.ng, Kayode, Ayinde2, Ibukun, Okegbade Ayobami1, A. A., Akomolafe3, Abosede, Oshuporu Opeoluwa1, Olawale, Koleoso Sunday3
Publikováno v:
Science World Journal. 2024, Vol. 19 Issue 2, p440-454. 15p.
Publikováno v:
Symmetry, Vol 16, Iss 4, p 469 (2024)
We address the estimation of regression parameters for the ill-conditioned predictive linear model in this study. Traditional least squares methods often encounter challenges in yielding reliable results when there is multicollinearity. Therefore, we
Externí odkaz:
https://doaj.org/article/e1127b775a6445eb9cd3f8abd1361504
Publikováno v:
African Scientific Reports, Pp 212-228 (2022)
Despite its common usage in estimating the linear regression model parameters, the ordinary least squares estimator often suffers a breakdown when two or more predictor variables are strongly correlated. This study proposes an alternative estimator t
Externí odkaz:
https://doaj.org/article/3481ef5410e8409e9e7bbf26872d9d43
Publikováno v:
African Scientific Reports, Pp 188-204 (2022)
The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient. To overcome the multicollinearity pro
Externí odkaz:
https://doaj.org/article/61dbf16135d146cda94a7faf66f0d692
Publikováno v:
African Scientific Reports, Pp 126-126 (2023)
Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables. In this case,
Externí odkaz:
https://doaj.org/article/ade1ae02586a48b892b5a86b74be48db
Publikováno v:
African Scientific Reports, Pp 123-123 (2023)
Correlated regressors are a major threat to the performance of the conventional ordinary least squares (OLS) estimator. The ridge estimator provides more stable estimates in this circumstance. However, both OLS and Ridge estimators are sensitive to o
Externí odkaz:
https://doaj.org/article/421c87f54fee4bec94bba41f795c1ca0
Autor:
Oluyemi A. Okunlola, Mohannad Alobid, Olusanya E. Olubusoye, Kayode Ayinde, Adewale F. Lukman, István Szűcs
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structu
Externí odkaz:
https://doaj.org/article/89c3a63ca7a04b74a86c9cd1c33f18e1
Publikováno v:
Journal of Nigerian Society of Physical Sciences, Vol 4, Iss 2 (2022)
The ordinary least square (OLS) method is very efficient in estimating the regression parameters in a linear regression model under classical assumptions. If the model contains outliers, the performance of the OLS estimator becomes imprecise. Multico
Externí odkaz:
https://doaj.org/article/675f97067152445a8330dfa5645d57d4