bootstrap model selectionoutlierrobust model selectionschwarz bayesian information criterionstratified bootstrapWe propose a new approach to the selection of regression models based on combining a robust penalized criterion and a robust conditional expected prediction loss function that is estimated using a ...
Bootstrap model selectionOutlierRobust model selectionSchwarz Bayesian information criterionStratified bootstrapWe propose a new approach to the selection of regression models based on combining a robust penalized criterion and a robust conditional expected prediction loss function that is estimated using a ...
Morgenthaler, S., R. E. Welsch, and A. Zenide, 2003. Algorithms for Robust Model Selection in Linear Regression, in Theory and Applications of Recent Robust Methods, eds. M Hubert, G. Pison, A. Struyf, and S. Van Aelst, Basel, Switzerland: Birkhauser-Verlag, 195-206....
Define a custom robust loss function that is robust to outliers to use in feature selection for regression: f(yi,yj)=1−exp(−∣yi−yj∣) Get customlossFcn = @(yi,yj) 1 - exp(-abs(yi-yj')); Tune the regularization parameter using the custom-defined robust loss function. Get...
On Model Selection in Robust Linear Regression Several model selection criteria which generally can be classified as the penalized robust method are studied in this paper. Particularly we derive a criterion based on Rissanen's stochastic complexity. Some asymptotic properties concern... Eidgenossische ...
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 ...
Variable Selection for Logistic Regression Using a Prediction Focussed Information Criterion In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the Focussed Information Cr... G Claeskens,C Croux,J Van Kerckhoven - 《...
Y., 2009, " Linear Regression Model Selection Based on Robust Bootstraping Technique" American Journal of Applied Sciences, 6(6), pp. 1191- 1198.Uraibi, Hassan S., Habshah Midi, Bashar A. Talib, and Jabar H. Yousif. "Linear Regression Model Selection Based on Robust Bootstrapping ...
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 ...
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 ...