Results 31 to 40 of about 132,600 (302)

Determining the Effect of Some Biasing Parameter Selection Methods for the Two Stage Ridge Regression Estimator

Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2018
The useof biased estimation techniques is inevitable in connection withmulticollinearity. Two stage ridge estimator is a pioneer biased estimatorwhich is use to recover the problems that are originated from themulticollinearity.
Selma Toker, Nimet Özbay
doaj   +1 more source

The Solution of Multicollinearity Problem via Biased Regression Analysis in Southern Anatolian Red Cattle

Turkish Journal of Agriculture: Food Science and Technology, 2022
The aim of this study is to investigate the effectiveness of biased estimation methods, principal component regression (PC) and ridge regression (RR) methods, according to unbiased the least squares (LS) method, against the multiple linearity problem ...
Hatice Hızlı
doaj   +1 more source

Multicollinearity [PDF]

American Journal of Orthodontics and Dentofacial Orthopedics, 2021
Michail, Tsagris, Nikolaos, Pandis
openaire   +3 more sources

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

Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation

Journal of New Theory, 2022
The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical ...
Tuğba Söküt Açar
doaj   +1 more source

Multicollinearity test results.

, 2023
Multicollinearity test results.
Ruijuan Du (12271565), Yu Sun (18463), Xiumei Xu (1786048), Xinmin Liu (408161)   +3 more
core   +1 more source

Improving generalized ridge estimator for the gamma regression model. [PDF]

المجلة العراقية للعلوم الاحصائية
It has been consistently proven that the ridge estimator is an effective shrinking strategy for reducing the effects of multicollinearity. An effective model to use when the response variable is positively skewed is the Gamma Regression Model (GRM ...
AVAN Al-Saffar, Zakaria Y. Algamal
doaj   +1 more source

Multicollinearity assessment output.

, 2022
Multicollinearity assessment output.
Diriba Ayala (10711955), Ginenus Fekadu (7137293), Dejene Seyoum (6081149), Ayantu Getahun (12219019), Diriba Mulisa (9470986), Haile Bikila (11827104), Tadesse Tolossa (9470983), Getahun Fetensa (8741844), Tesfaye Shibiru (11827107), Lami Bayisa (10331792), Markos Desalegn (12883888), Emiru Merdassa Atomssa (7610417), Bizuneh Wakuma (9389193), Ebisa Turi (9264606)   +13 more
core   +1 more source

Condition-index based new ridge regression estimator for linear regression model with multicollinearity

Kuwait Journal of Science, 2023
Ridge regression is employed to estimate the regression parameters while circumventing the multicollinearity among independent variables. The ridge parameter plays a vital role as it controls bias-variance tradeoff. Several methods for choosing the ridge
Irum Sajjad Dar, Sohail Chand, Maha Shabbir, B.M. Golam Kibria   +3 more
doaj   +1 more source

Do instructional attributes pose multicollinearity problems? An empirical exploration [PDF]

, 2010
It is commonly perceived that variables ‘measuring’ different dimensions of teaching (construed as instructional attributes) used in student evaluation of teaching (SET) questionnaires are so highly correlated that they pose a serious multicollinearity ...
Nghiemb, Hong Son, Nghiem, Hong Son, Alauddin, Mohammad   +2 more
core   +1 more source