Results 21 to 30 of about 56,202 (281)
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
core +2 more sources
Mathematics, 2023 In the longitudinal data analysis we integrate flexible linear predictor link function and high-correlated predictor variables. Our approach uses B-splines for non-parametric part in the linear predictor component.
Mozhgan Taavoni +2 more
doaj +1 more source
Bayesian Tobit quantile regression using-prior distribution with ridge parameter [PDF]
, 2014 A Bayesian approach is proposed for coefficient estimation in the Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients.
Bilias Y +5 more
core +1 more source
Nonlinear Generalized Ridge Regression
, 2023 A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relationship between a y-outcome variable and each of p = 2 or more potential x-predictor variables. The y-outcome is then predicted from all p of these "linearized" spline-predictors using the form of Generalized Ridge Regression that is most likely to yield ...
openaire +2 more sources
Mathematics, 2023 This study proposes an intensive longitudinal functional model with multiple time-varying scales and subject-specific random intercepts through mixed model equivalence that includes multiple functional predictors, one or more scalar covariates, and one ...
Mostafa Zahed +2 more
doaj +1 more source
Nonparametric Generalized Ridge Regression
, 2023 The title of this paper is potentially misleading, its methods are neither innovative nor easy to apply, and its numerical example is nearly ...
openaire +2 more sources
Journal of Nigerian Society of Physical Sciences, 2023 This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression RSUR and proposes the Kibria-Lukman KLSUR estimator for the parameters of the Seemingly Unrelated Regression (SUR) model when the ...
Oluwayemisi Oyeronke Alaba +1 more
doaj +1 more source
Penalized Regression with Ordinal Predictors [PDF]
, 2008 Ordered categorial predictors are a common case in regression modeling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this article penalized regression techniques are proposed.
Gertheiss, Jan, Tutz, Gerhard
core +1 more source
مجلة بغداد للعلوم, 2019 In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in ...
Saja Mohammad Hussein
doaj +1 more source
LASSO type penalized spline regression for binary data
BMC Medical Research Methodology, 2021 Background Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects.
Muhammad Abu Shadeque Mullah +2 more
doaj +1 more source