(1985). Robust model selection in regression. Statistics & Probability Letters, 3(1), 21-23.E. Ronchetti. Robust model selection in regression. Statistics & Probability Letters, 3:21-23, 1985.Ronchetti, E. (1985). Robust model selection in regression. Stat. Probab. Lett. 3, 21-23....
Algorithms for robust model selection in linear regression. In: M. Hubert, G. Pison, A. Struyf, and S. Van Aelst (Eds.), Theory and Applications of Recent Robust Meth- ods, Birkh¨auser-Verlag, Basel (Switzerland), 195-206.Morgenthaler, S., Welsch, R.E., Zenide, A. Algorithms ...
The performance of the feature selection algorithm highly depends on the value of the regularization parameter. A good practice is to tune the regularization parameter for the best value to use in feature selection. Tune the regularization parameter using five-fold cross validation. Use the mean squ...
In linear regression, outliers and leverage points often have large influence in the model selection process. Such cases are downweighted with Mallows-type weights here, during estimation of submodel parameters by generalised M-estimation. A robust version of Mallows's Cp (Ronchetti &. Staudte, ...
The paper considers the problem of estimating a periodic function in a continuous time regression model observed under a general semimartingale noise with an unknown distribution in the case when continuous observation cannot be provided and only discrete time measurements are available. Two specific type...
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 ...
A new method for estimation and model selection: $$ho $$ ρ -estimation The aim of this paper is to present a new estimation procedure that can be applied in various statistical frameworks including density and regression and w... Y.,Baraud,L.,... - 《Inventiones Mathematicae》 被引量:...
A minimum description length (MDL) and stochastic complexity approach for model selection in robust linear regression is studied in this paper. Computational aspects and implementation of this approach to practical problems are the focuses of the study. Particularly, we provide both algorithms and a ...
In this paper, we research the regression problem of time series data from heterogeneous populations on the basis of the finite mixture regression model. W... J Liu,W Ye - 《理论数学进展(英文)》 被引量: 0发表: 2020年 Variable Selection in Finite Mixture of Regression Models Variable select...
In linear regression, outliers and leverage points often have large influence in the model selection process. Such cases are downweighted with Mallows-type... S Sommer,RG Staudte - 《Australian & New Zealand Journal of Statistics》 被引量: 9发表: 2010年 Robust nonnegative garrote variable select...