Regression analysisRobust model selectionIn recent years, in the literature of linear regression models, robust model selection methods have received increasing attention when the datasets contain even a small
Algorithms for robust model selection in lin- ear regression. In M. Hubert, G. Pison, A. Struyf, and S. Van Aelst, editors, Theory and Applications of Recent Robust Methods, pages 195-206. Birkha¨user-Verlag, Basel (Switzerland), 2003.Morgenthaler, S., R. E. Welsch, and A. Zen...
In this paper, we introduce a robust variable selection procedure for FMR models using the t distribution. With appropriate selection of the tuning parameters, the consistency and the oracle property of the regularized estimators are established. To estimate the parameters of the model, we develop ...
Robust estimation and variable selection in heteroscedastic regression model using least favorable distributionThe assumption of equal variances is not always appropriate and different approaches for modelling variance heterogeneity have been widely studied in the literature. One of these approaches is joint ...
model selectionnonhomogeneous datapolynomial regressionrobustnessWhen selecting a model, robustness is a desirable property. However, most model selection criteria that are based on the Kullback鈥揕eibler divergence tend to have reduced performance when the data are contaminated by outliers. In this ...
Spatial autoregressive modelExponential squared lossOracle propertyAdaptive lassoVariable selectionSpatial dependent data frequently occur in spatial econometrics and endemiology. In this work, we propose a class of penalized robust regression estimators based on exponential squared loss with independent and ...
Y., 2009, " Linear Regression Model Selection Based on Robust Bootstraping Technique" American Journal of Applied Sciences, 6(6), pp. 1191- 1198.Hassan S. Uraibi, Habshah Midi, Bashar A. Talib and Jabar H. Yousif , Linear Regression Model Selection Based On Robust Bootstrapping ...
Fig. 3: Regression results. Model parameters, shown in (a–c) are consistently well estimated from experimental data for a range of Reynolds numbers Re, particularly when the amplitude of flow time dependence is sufficiently large, as illustrated in (d) and (e). For the results shown, flows...
By using several algorithm in order to make the good decision is also a good solution to avoid over-fitting. In order to avoid overfitting in a algorithm, it is necessary to use additional techniques on parameters: Regularization (Dimension reduction with model selection) Early stopping prun...
Model selectionOracle propertySCADVarying coefficient modelWeighted composite quantile regressionIn this paper, a new variable selection procedure based on weighted composite quantile regression is proposed for varying coefficient models with a diverging number of parameters. The proposed method is based on ...