Variable selection in linear regression based on ridge estimator

Dorugade, A. V.; Kashid, D. N.

doi:10.1080/00949650903012413

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…Step 5. New velocities and positions of the particles are calculated by using the Equations given in (26) and (27).…”

Section: Algorithmmentioning

confidence: 99%

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Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression

Baş¹

2017

Trends Comput Sci Inf Technol

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“…Step 5. New velocities and positions of the particles are calculated by using the Equations given in (26) and (27).…”

Section: Algorithmmentioning

confidence: 99%

“…And also, there are many methods in the literature for ridge regression [23][24][25][26][27][28][29]. And also, [30] proposed some new methods that take care of the skewed eigenvalues of the matrix of explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression

Baş¹

2017

Trends Comput Sci Inf Technol

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“…To overcome the problem of extremely time consuming, Salibian‐Barrera and Van Aelst (2008) propose a fast and robust bootstrap method of robust model selection. Besides, Kashid and Kulkarni (2002), Dorugade and Kashid (2010) and Jadhav et al (2014) propose a general framework of model selection in the presence of outliers and multicollinearity.…”

Section: Introductionmentioning

confidence: 99%

Outlier robust model averaging based on S_p criterion

Guo

2021

Stat

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In this paper, we consider the problem of obtaining appropriate weights for model averaging in the face of model uncertainty and outliers. Most of the current model averaging methods are based on ordinary least squares (OLS) estimators, which are very sensitive to outliers or departures from the normality assumption on the error distribution. To overcome this problem, we propose an outlier robust Sp‐type model averaging (SMA) procedure whenever data contain outliers. The proposed method combines the advantages of model averaging and robust model selection. Therefore, it performs better than other classical model averaging and robust model section methods, especially in the outlier data case, and nearly the same as Mallows's model averaging (MMA) even in the clean data case. Moreover, the proposed criterion is quadratic in the weights, so it is operationally simple to implement. The performance of the proposed method is illustrated through some extensive simulation studies as well as real data examples.

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“…However, these methods can become time consuming and inconsistent when there are many independent variables to choose from, and the F-test can only be used when the model error structure is normally distributed. Variance inflation factor (VIF) and ridge regression have often been used to select the best combination of explanatory variables that are correlated (Hoerl 1962;Hoerl and Kennard 1970;Marquardt 1970;Graham 2003;Montgomery et al 2006;Dorugade and Kashid 2010).…”

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confidence: 99%

Catch‐Rate Standardization for Yellow Perch in Lake Erie: A Comparison of the Spatial Generalized Linear Model and the Generalized Additive Model

Jiao

Winter

2011

Trans Am Fish Soc

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Various statistical models have been used to standardize catch rates of fish populations for stock assessment. The generalized linear model (GLM) is one of the most commonly used approaches. However, response variables in fisheries data are often spatially autocorrelated or show a nonlinear relationship with explanatory variables, thus violating the underlying assumption of the GLM. The GLM with spatial autocorrelation (s‐GLM) and the generalized additive model (GAM) may be better suited to dealing with autocorrelated variables and nonlinear relationships, respectively. In this study, catch rates of yellow perch Perca flavescens from a fishery‐independent gill‐net survey in Lake Erie during 1990–2003 were estimated by the s‐GLM and GAM. Set duration, gear depth, temperature at gear depth, dissolved oxygen concentration, water transparency, and latitude were selected as significant explanatory variables for these models. By comparing the goodness of fit, reduction of spatial autocorrelation, and prediction errors from cross validation, we found that the GAM had the best goodness of fit and lowest prediction errors but the s‐GLM resulted in the greatest reduction in spatial autocorrelation for catch‐rate standardization of yellow perch in Lake Erie. The GAM is more suitable for modeling the nonlinear relationships between species characteristics and explanatory variables, and the s‐GLM is preferable for dealing with spatially autocorrelated residuals. Application of the GAM or s‐GLM is determined by the species distribution pattern and available explanatory variables.

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Variable selection in linear regression based on ridge estimator

Cited by 11 publications

References 6 publications

Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression

Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression

Outlier robust model averaging based on S_p criterion

Catch‐Rate Standardization for Yellow Perch in Lake Erie: A Comparison of the Spatial Generalized Linear Model and the Generalized Additive Model

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Variable selection in linear regression based on ridge estimator

Cited by 11 publications

References 6 publications

Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression

Shrinkage Parameters for Each Explanatory Variable Found Via Particle Swarm Optimization in Ridge Regression

Outlier robust model averaging based on Sp criterion

Catch‐Rate Standardization for Yellow Perch in Lake Erie: A Comparison of the Spatial Generalized Linear Model and the Generalized Additive Model

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Outlier robust model averaging based on S_p criterion