Results 261 to 270 of about 34,185 (288)

On some improved ridge estimators

Statistische Hefte, 1987
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Singh, Balvir, Chaubey, Yogendra P.
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On the Strong Consistency of Ridge Estimates

Communications in Statistics - Theory and Methods, 2014
In this article, we establish strong consistency of the ridge estimates using extended results for the strong consistency of the least squares estimates in multiple regression models which discard the usual assumption of null mean value for the errors and only requires them to be i.i.d. with absolute moment of order r (0 < r ⩽ 1).
João Lita Da Silva, João Tiago Mexia, Luís Pedro Ramos   +2 more
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A note on generalized ridge estimators

Communications in Statistics - Theory and Methods, 1990
It is shown that a necessary and sufficient condition derived by Farebrother (1984)for a generalized ridge estimator to dominate the ordinary least-squares estimator with respect to the mean-square-error-matrix criterion in the linear regression model admits a similar interpretation as the well known criterion of Toro-Viz-carrondo and Wallace (1968)for
Jerzy K. Baksalary, Pawel R. Pordzik, Götz Trenkler   +2 more
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Ridge regression. discussion and comparison of seven Ridge estimators

2013
In the paper, the characteristics of seven different techniques of Ridge Regression are evaluated with respect to the same model. A consumption function with yearly data for Greece is therefore analysed and Monte-Carlo method s employed to check the performance of the estimation methods.
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POISSON RIDGE REGRESSION ESTIMATORS

Advances and Applications in Statistics
This paper proposes a new Poisson ridge regression estimator using grid search. The new and known ridge estimators were then compared based on MSE criterion using Monte Carlo simulation. Different values of parameters were considered, such as sample size of greater than or equal to 10; correlation values of 0.85 to 0.99; and number of explanatory ...
Jerson S. Mohamad, Angelyn S. Delica, Shiela May M. Ledesma   +2 more
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On optimum experimental design for ridge estimates

Kybernetika, 1988
This paper deals with a new optimality criterion in regression experiments. A method of minimizing the summary variance of a ridge estimate of a vector of parameters, under the condition that the norm of the bias divided by the norm of the vector parameter is bounded from above, is given.
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ALTERNATIVE TO RIDGE ESTIMATOR WITHOUT RIDGE PARAMETER

2015
The problem of estimation of the regression coefficients in a multiple regression model is considered under multicollinearity, heteroscedasticity and outlier situations. This paper introduces two estimators for regression parameters. The merit of the proposed estimators is that it does not require estimating the ridge parameter unlike other existing ...
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Ridge Type M-Estimators

1985
In this paper we introduce a new class of estimators, ridge type M-estimators, designed for analyzing linear regression models when regressor variables are multicollinear and residual distributions display long tails. The estimators are defined as weighted maximum likelihood type (M-) estimators when additional information about the parameters is given.
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On the performance of two parameter ridge estimator under the mean square error criterion

Applied Mathematics and Computation, 2013
Selma Toker
exaly  

Modified ridge‐type estimator to combat multicollinearity: Application to chemical data

Journal of Chemometrics, 2019
Adewale F Lukman, Kayode Ayinde
exaly