Results 41 to 50 of about 5,491 (290)

On the performance of some new ridge parameter estimators in the Poisson-inverse Gaussian ridge regression

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, Muhammad Amin, Ahmed Elhassanein   +2 more
doaj   +1 more source

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   +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

Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints [PDF]

, 2009
In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category.
Tutz, Gerhard, Zahid, Faisal Maqbool
core   +1 more source

Ordinal Ridge Regression with Categorical Predictors [PDF]

, 2011
In multi-category response models categories are often ordered. In case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to ...
Zahid, Faisal Maqbool
core   +1 more source

New estimators in a partial linear model depending on an unbiased ridge regression estimator [PDF]

EPJ Web of Conferences
This 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., AIheety Mustafa I.   +1 more
doaj   +1 more source

Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation

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
doaj   +1 more source

Ridge Estimator Revisited [PDF]

, 2012
summary:Bad conditioned matrix of normal equations in connection with small values of model parameters is a source of problems in parameter estimation. One solution gives the ridge estimator. Some modification of it is the aim of the paper. The behaviour
Kubáček, Lubomír
core   +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

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 2; peer review: 2 approved, 1 approved with reservations]

F1000Research, 2021
Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Olukayode Adebimpe, Benedicta B. Aladeitan, Adewale F. Lukman, Oluwakemi E. Abiodun, Olajumoke Oludoun   +4 more
doaj   +1 more source