Results 261 to 270 of about 33,950 (290)

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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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
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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.
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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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Bayes minimax ridge regression estimators

Communications in Statistics - Theory and Methods, 2018
ABSTRACTThe problem of estimating of the vector β of the linear regression model y = Aβ + ϵ with ϵ ∼ Np(0, σ2Ip) under quadratic loss function is considered when common variance σ2 is unknown.
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Characterization of general ridge estimators

Statistics & Probability Letters, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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BAYESIAN ESTIMATION OF T–G RIDGE MODEL

Statistica Neerlandica, 1984
AbstractRidge type analysis of the Theil–Goldberger mixed model, considered earlier by Saxena and Bhatta–charya (1983) for the non–Bayesian set–up, is discussed from the Bayesian view–point when a has a closed prior and the loss–function being squared error.
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On the estimation of Bell regression model using ridge estimator

Communications in Statistics Part B: Simulation and Computation, 2023
Muhammad Amin, Muhammad Nauman Akram, Abdul Majid   +2 more
exaly  

Quantile-based robust ridge m-estimator for linear regression model in presence of multicollinearity and outliers

Communications in Statistics Part B: Simulation and Computation, 2021
Muhammad Suhail, Sohail Chand, B M Golam Kibria   +2 more
exaly  

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

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