Results 21 to 30 of about 4,296 (259)
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
openaire +3 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
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
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 ...
openaire +2 more sources
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
openaire +2 more sources
Alexandria Engineering Journal, 2023 The Poisson Inverse Gaussian Regression model (PIGRM) is used for modeling the count datasets to deal with the issue of over-dispersion. Generally, the maximum likelihood estimator (MLE) is used to estimate the PIGRM estimates.
Asia Batool +2 more
doaj +1 more source
A class of generalized ridge estimators
Communications in Statistics - Simulation and Computation, 2017 ABSTRACTPresence of collinearity among the explanatory variables results in larger standard errors of parameters estimated.
Bhat, Satish, Vidya, R.
openaire +2 more sources
Evaluation of Two Stage Modified Ridge Estimator and Its Performance
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2018 Biasedestimation methods are more desirable than two stage least squares estimationfor simultaneous equations models suffering from the problem ofmulticollinearity.
Selma Toker, Nimet Özbay
doaj +1 more source
New estimators in a partial linear model depending on an unbiased ridge regression estimator [PDF]
EPJ Web of ConferencesThis paper introduces two new estimators based on the philosophy of unbiased ridge regression estimation, where the parameters are part of a partial linear model suffering from multicollinearity.
Al-Khazraji Yousif A. +1 more
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