Results 21 to 30 of about 835 (216)

A Modified Two Parameter Estimator with Different Forms of Biasing Parameters in the Linear Regression Model

African Scientific Reports, 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.
Abiola T. Owolabi, Kayode Ayinde, Olusegun O. Alabi   +2 more
doaj   +1 more source

Combining modified ridge-type and principal component regression estimators

Scientific African, 2020
The performance of ordinary least squares estimator (OLSE) when there is multicollinearity (MC) in a linear regression model becomes inefficient. The principal components regression and the modified ridge-type estimator have been proposed at a different ...
Adewale F. Lukman, Kayode Ayinde, Olajumoke Oludoun, Clement A. Onate   +3 more
doaj   +1 more source

KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS

E-Jurnal Matematika, 2014
Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached.
HANY DEVITA, I KOMANG GDE SUKARSA, I PUTU EKA N. KENCANA   +2 more
doaj   +1 more source

Unbiased K-L estimator for the linear regression model [version 1; peer review: 2 approved, 1 approved with reservations]

F1000Research, 2021
Background: In the linear regression model, the ordinary least square (OLS) estimator performance drops when multicollinearity is present. According to the Gauss-Markov theorem, the estimator remains unbiased when there is multicollinearity, but the ...
BENEDICTA Aladeitan, Adewale F Lukman, Esther Davids, Ebele H Oranye, Golam B M Kibria   +4 more
doaj   +1 more source

An unbiased estimator with prior information

Arab Journal of Basic and Applied Sciences, 2020
The ordinary least square (OLS) estimator suffers a breakdown in the presence of multicollinearity. The estimator is still unbiased but possesses a significant variance.
Adewale F. Lukman, Kayode Ayinde, Benedicta Aladeitan, Rasak Bamidele   +3 more
doaj   +1 more source

Generalized Kibria-Lukman Estimator: Method, Simulation, and Application

Frontiers in Applied Mathematics and Statistics, 2022
In the linear regression model, the multicollinearity effects on the ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) estimator is a recent estimator that has been
Issam Dawoud, Mohamed R. Abonazel, Fuad A. Awwad   +2 more
doaj   +1 more source

Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model

Journal of Applied Mathematics, 2014
Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modified r-k class estimator.
Jibo Wu
doaj   +1 more source

The VIF and MSE in Raise Regression

Mathematics, 2020
The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after
Román Salmerón Gómez, Ainara Rodríguez Sánchez, Catalina García García, José García Pérez   +3 more
doaj   +1 more source

On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model

African Scientific Reports, 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.
Janet Iyabo Idowu, Olasunkanmi James Oladapo, Abiola Timothy Owolabi, Kayode Ayinde   +3 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source