Results 251 to 260 of about 548,762 (295)

A jackknifed ridge estimator in probit regression model

, 2020
In this study, the effects of multicollinearity on the maximum likelihood estimator are analyzed in the probit regression model. It is known that the near-linear dependencies in the design matrix affect the maximum likelihood estimation negatively ...
Yasin Asar, K. Kılınç
semanticscholar   +1 more source

Shrinkage Ridge Estimators in Linear Regression

Communications in Statistics - Simulation and Computation, 2013
The problem of estimation of the regression coefficients in a multiple regression model (MRM) is considered under multicollinearity situation. Further it is suspected that the regression coefficients may be restricted to a subspace. In this approach, we present the estimators of the regression coefficients combining the idea of preliminary test ...
Mohammad Arashi, S. M. M. Tabatabaey, M. Hassanzadeh Bashtian   +2 more
openaire   +1 more source

Unbiased ridge estimation with prior information and ridge trace

Communications in Statistics - Theory and Methods, 1995
A procedure is illustrated to incorporate prior information in the ridge regression model. Unbiased ridge estimators with prior information are defined and a robust estimate of the ridge parameter k is proposed.
Robert H. Crouse, null Chun Jin, R. C. Hanumara   +2 more
openaire   +1 more source

A ‘conservative’ ridge estimator

Economics Letters, 1979
Abstract In this paper an alternative to the Ordinary Ridge Estimator (ORE) introduced by Hoerl and Kennard (1970) is proposed. This estimator is called a ‘Conservative’ Ridge Estimator (CRE), because it puts a heavier weight on the unbiasedness and a smaller weight on the statistical stability of the ‘unstable’ estimation components than the ORE ...
openaire   +1 more source

A new biased estimator based on ridge estimation

Statistical Papers, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sakallioglu S., Kaçiranlar S.
openaire   +2 more sources

Adaptive Weighted Ridge Regression Estimator for Time-Varying Sensitivity Identification

IEEE Transactions on Power Systems
This letter proposes an adaptive weighted ridge regression estimator for the identification of time-varying sensitivity. By decoupling the effect of forgetting factor and ridge parameter, we develop the online adaptive rules for these two parameters via ...
Zhiyuan Tang, You-bo Liu, Tingjian Liu, Xiao Xu, Junyong Liu   +4 more
semanticscholar   +1 more source

A new method for choosing the biasing parameter in ridge estimator for generalized linear model

Chemometrics and Intelligent Laboratory Systems, 2018
Multicollinearity problem arises frequently in several modern applications, such as chemometrics, biology, and other scientific fields. The common feature of the multicollinearity problem is that a large number of predictors are highly correlated ...
Z. Algamal
semanticscholar   +1 more source

Mean squared error comparisons of the modified ridge regression estimator and iiie restricted ridge regression estimator

Communications in Statistics - Theory and Methods, 1998
Swindel (1976) introduced a modified ridge regression estimator based on prior information. Sarkar (1992) suggested a new estimator by combining in a particular way the two approaches followed in obtaining the restricted ieast squares and ordinary ndge regression estimators.
Kaçiranlar S., Sakallioglu S., Akdeniz F.   +2 more
openaire   +2 more sources

Ridge estimator with correlated errors and two-stage ridge estimator under inequality restrictions

Communications in Statistics - Theory and Methods, 2016
AbstractLiew (1976a) introduced generalized inequality constrained least squares (GICLS) estimator and inequality constrained two-stage and three-stage least squares estimators by reducing primal–dual relation to problem of Dantzig and Cottle (1967), Cottle and Dantzig (1974) and solving with Lemke (1962) algorithm.
Toker S., Kaçıranlar S.
openaire   +2 more sources

A Tobit Ridge Regression Estimator

Communications in Statistics - Theory and Methods, 2013
This article analyzes the effects of multicollienarity on the maximum likelihood (ML) estimator for the Tobit regression model. Furthermore, a ridge regression (RR) estimator is proposed since the mean squared error (MSE) of ML becomes inflated when the regressors are collinear. To investigate the performance of the traditional ML and the RR approaches
G. Khalaf, K. Månsson, P. Sjölander, G. Shukur   +3 more
openaire   +1 more source