6 documents found, page 1 of 1
A robust sparce linear approach for contamined data
Shahriari, Shirin; Faria, Susana; Gonçalves, A. ManuelaA challenging problem in a linear regression model is to select a parsimonious model which is robust to the presence of contamination in the data. In this paper, we present a sparse linear approach which detects outliers by using a highly robust regression method. The model with optimal predictive ability as measured by the median absolute deviation of the prediction errors on JackKnife subsets is used to detec...
Selecção robusta em modelos de regressão linear com um grande número de preditores
Shahriari, Shirin; Faria, Susana; Gonçalves, A. ManuelaNeste trabalho discute-se o problema de selecção de variáveis em modelos de regressão linear que envolvem um grande número de preditores, contaminados por outliers e observações atípicas. Como os métodos clássicos de selecção de variáveis não são resistentes à presença de outliers e outros tipos de contaminação, neste estudo são estudados métodos robustos de selecção de variáveis em modelos de regressão linear ...
Variable selection methods in high-dimensional regression: a simulation study
Shahriari, Shirin; Faria, Susana; Gonçalves, A. ManuelaA challenging problem in the analysis of high-dimensional data is variable selection. In this study, we describe a bootstrap based technique for selecting predictors in partial least-squares regression (PLSR) and principle component regression (PCR) in high-dimensional data. Using a bootstrap-based technique for significance tests of the regression coefficients, a subset of the original variables can be selecte...
Outlier detection and robust variable selection for least angle regression
Shahriari, Shirin; Faria, Susana; Gonçalves, A. Manuela; Van Aelst, StefanThe problem of selecting a parsimonious subset of variables from a large number of predictors in a regression model is a topic of high importance. When the data contains vertical outliers and/or leverage points, outlier detection and variable selection are inseparable problems. Therefore a robust method that can simultaneously detect outliers and select variables is needed. An outlier detection and robust varia...
Variable selection in linear regression models with large number of predictors
Shahriari, ShirinIn this thesis, we study the problem of variable selection in linear regression models in the presence of a large number of predictors. Usually, some of these predictors are correlated, so including all of them in a regression model will not essentially improve the model's predictive ability. Also, models with reasonable and tractable amount of predictors are easier to interpret than models with a large number ...
Robust linear model selection in high dimensional data
Shahriari, Shirin; Faria, Susana; Gonçalves, A. ManuelaIn this work we consider the problem of selecting variables from a potentially large number of predictors in a regression model when outliers and atypical observations are embedded in data. Since classical variable selection methods are not resistant to the presence of outliers and other contaminations, well established robust variable selection methods in high dimensional data sets are studied. Different simul...