Results 11 to 20 of about 1,043 (255)

Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation [PDF]

Surveys in Mathematics and its Applications, 2009
Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR).
Sharad Damodar Gore, Feras Sh. M. Batah
doaj   +3 more sources

A New Mixed Biased Estimator for Ill‐Conditioning Challenges in Linear Regression Model With Chemometrics Applications [PDF]

Analytical Science Advances
In linear regression models, the ordinary least squares (OLS) method is used to estimate the unknown regression coefficients. However, the OLS estimator may provide unreliable estimates in non‐orthogonal models.
Muhammad Amin, Sadiah M. A. Aljeddani, Muhammad Nauman Akram, Sajida Yasmeen   +3 more
doaj   +2 more sources

M Robust Weighted Ridge Estimator in Linear Regression Model

African Scientific Reports, 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.
Taiwo Stephen Fayose, Kayode Ayinde, Olatayo Olusegun Alabi   +2 more
doaj   +3 more sources

Robust-stein estimator for overcoming outliers and multicollinearity [PDF]

Scientific Reports, 2023
Linear regression models with correlated regressors can negatively impact the performance of ordinary least squares estimators. The Stein and ridge estimators have been proposed as alternative techniques to improve estimation accuracy.
Adewale F. Lukman, Rasha A. Farghali, B. M. Golam Kibria, Okunlola A. Oluyemi   +3 more
doaj   +2 more sources

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   +4 more sources

Bootstrap-quantile ridge estimator for linear regression with applications. [PDF]

PLoS ONE
Bootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the ...
Irum Sajjad Dar, Sohail Chand
doaj   +2 more sources

Superiority of the MCRR Estimator Over Some Estimators In A Linear Model [PDF]

المجلة العراقية للعلوم الاحصائية, 2011
Modified (r, k) class ridge regression (MCRR) which includes unbiased ridge regression (URR), (r, k) class, principal components regression (PCR) and the ordinary least squares (OLS) estimators is proposed in regression analysis, to overcome the problem ...
Feras Sh. M. Batah
doaj   +1 more source

Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]

Journal of Statistical Theory and Applications (JSTA), 2015
This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
doaj   +1 more source

A new hybrid estimator for linear regression model analysis: Computations and simulations

Scientific African, 2023
The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE).
G.A. Shewa, F.I. Ugwuowo
doaj   +1 more source

Treating Multicollinearity Problem Using Gool Programming Technique [PDF]

The Egyptian Statistical Journal, 2011
Multiple regression analysis is usually efficient for prediction, but often produces poor results because of the multicollinearity among the independent variables.
Afaf El-Dash, Aly El-Hefnawy, Rasha Farghali   +2 more
doaj   +1 more source