Fast Robust Model Selection in Large Datasets

Large datasets are increasingly common in many research fields. In particular, in the linear regression context, it is often the case that a huge number of potential covariates are available to explain a response variable, and the first step of a reasonable statistical analysis is to reduce the numb...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 106; no. 493; pp. 203 - 212
Main Authors:	Dupuis, Debbie J., Victoria-Feser, Maria-Pia
Format:	Journal Article
Language:	English
Published:	Alexandria, VA Taylor & Francis 01-03-2011 American Statistical Association Taylor & Francis Ltd
Subjects:	Applications Applied statistics Computational methods Correlations Criteria Data analysis Data processing Datasets Estimation bias Estimators Exact sciences and technology False discovery rate General topics Least angle regression Linear inference, regression Linear regression M-estimator Mathematics Modeling Multicollinearity Outliers Parameter estimation Parametric inference Partial correlation Probability and statistics Random forests Random variables Regression analysis Robust t test Sciences and techniques of general use Statistical analysis Statistics Theory and Methods Rank statistic Correlation Forests Statistical distribution Sample size Bias Estimator robustness M-estimator Multivariate analysis Covariate Statistical simulation Parametric method Outlier Statistical test Least squares method Least angle regression Selection method Approximation theory Fast algorithm Sample survey Variable selection Discriminant analysis Statistical analysis Linear regression Model selection Statistical association Statistical estimation Random forests T statistic Statistical method Statistical regression Selection problem Sampling theory Correlation analysis Multicollinearity False discovery rate Robust t test M estimation Biased estimation Partial correlation
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