Results 31 to 40 of about 5,491 (290)
Another Look at Partitioned Ridge Regression Estimators [PDF]
The Egyptian Statistical Journal, 1992 Several biased estimators have been proposed as alternatives to the Least squares estimator when multicollinearity is present in the multiple linear regression model.
Linda Abskharoon, Mahmoud Mahmoud
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Smoothly adaptively centered ridge estimator [PDF]
Journal of Multivariate Analysis, 2022 With a focus on linear models with smooth functional covariates, we propose a penalization framework (SACR) based on the nonzero centered ridge, where the center of the penalty is optimally reweighted in a supervised way, starting from the ordinary ridge solution as the initial centerfunction.
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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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Nonparametric ridge estimation
The Annals of Statistics, 2014 Published in at http://dx.doi.org/10.1214/14-AOS1218 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Genovese Christopher R. +3 more
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Algorithms for ridge estimation with convergence guarantees
J. Mach. Learn. Res., 2021 The extraction of filamentary structure from a point cloud is discussed. The filaments are modeled as ridge lines or higher dimensional ridges of an underlying density. We propose two novel algorithms, and provide theoretical guarantees for their convergences, by which we mean that the algorithms can asymptotically recover the full ridge set.
Wanli Qiao, Wolfgang Polonik
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A New Tobit Ridge-Type Estimator of the Censored Regression Model With Multicollinearity Problem
Frontiers in Applied Mathematics and Statistics, 2022 In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem.
Issam Dawoud +3 more
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Minimax Ridge Regression Estimation. [PDF]
The Annals of Statistics, 1977 The technique of ridge regression, first proposed by Hoerl and Kennard, has become a popular tool for data analysts faced with a high degree of multicollinearity in their data. By using a ridge estimator, one hopes to both stabilize one's estimates (lower the condition number of the design matrix) and improve upon the squared error loss of the least ...
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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
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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
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Generalized Mode and Ridge Estimation
CoRR, 2014 The generalized density is a product of a density function and a weight function. For example, the average local brightness of an astronomical image is the probability of finding a galaxy times the mean brightness of the galaxy. We propose a method for studying the geometric structure of generalized densities.
Yen-Chi Chen +2 more
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