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Algorithms for ridge estimation with convergence guarantees
CoRR, 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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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
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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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Applications of Some Improved Estimators in Linear Regression [PDF]
, 2005 The problem of estimation of the regression coefficients under multicollinearity situation for the restricted linear model is discussed. Some improve estimators are considered, including the unrestricted ridge regression estimator (URRE), restricted ...
Kibria, B. M. Golam
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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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Boosting Ridge Regression [PDF]
, 2005 Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques.
Binder, Harald, Tutz, Gerhard
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The effect of high leverage points on the logistic ridge regression estimator having multicollinearity [PDF]
, 2013 This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points.
Ariffin @ Mat Zin, Syaiba Balqish +1 more
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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
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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.
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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
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