Results 21 to 30 of about 300,687 (184)
American Journal of Orthodontics and Dentofacial Orthopedics, 2021 Michail, Tsagris, Nikolaos, Pandis
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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
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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
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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 +3 more
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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
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Role of Categorical Variables in Multicollinearity in the Linear Regression Model [PDF]
, 2007 The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory variables and analyzes
---, Shalabh +2 more
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Improving generalized ridge estimator for the gamma regression model. [PDF]
المجلة العراقية للعلوم الاحصائيةIt 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
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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 +3 more
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Collinearity diagnostics of binary logistic regression model [PDF]
, 2010 Multicollinearity is a statistical phenomenon in which predictor variables in a logistic regression model are highly correlated. It is not uncommon when there are a large number of covariates in the model.
Midi, Habshah +2 more
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Barekeng, 2022 The Program for International Student Assessment (PISA), becomes one of the references or indicators used to assess the development of students' knowledge and skills in each member country of the Organization for Economic Cooperation and Development ...
Vera Maya Santi +3 more
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