Results 11 to 20 of about 4,586 (136)
, 2021 In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical prediction models, the point estimates of the prediction accuracy ...
Furukawa, Toshi A. +4 more
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The Influence Function of Penalized Regression Estimators [PDF]
, 2014 To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers.
Alfons, Andreas +2 more
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Covariance Estimation: The GLM and Regularization Perspectives [PDF]
, 2011 Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data ...
Pourahmadi, Mohsen
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Penalized Regression with Correlation Based Penalty [PDF]
, 2006 A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors.
Tutz, Gerhard, Ulbricht, Jan
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Post Selection Shrinkage Estimation for High Dimensional Data Analysis
, 2016 In high-dimensional data settings where $p\gg n$, many penalized regularization approaches were studied for simultaneous variable selection and estimation.
Ahmed, S. E., Feng, Yang, Gao, Xiaoli
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, 2011 We propose a computationally intensive method, the random lasso method, for variable selection in linear models. The method consists of two major steps.
Nan, Bin +3 more
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Sparsity with sign-coherent groups of variables via the cooperative-Lasso [PDF]
, 2012 We consider the problems of estimation and selection of parameters endowed with a known group structure, when the groups are assumed to be sign-coherent, that is, gathering either nonnegative, nonpositive or null parameters.
Charbonnier, Camille +2 more
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Robust Identification of Target Genes and Outliers in Triple-negative Breast Cancer Data [PDF]
, 2018 Correct classification of breast cancer sub-types is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer (TNBC) which has the worst prognosis among breast cancer types.
Casimiro, Sandra +4 more
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Penalty Strategies in Semiparametric Regression Models
Mathematical and Computational ApplicationsThis study includes a comprehensive evaluation of six penalty estimation strategies for partially linear models (PLRMs), focusing on their performance in the presence of multicollinearity and their ability to handle both parametric and nonparametric ...
Ayuba Jack Alhassan +3 more
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On Bayesian robust regression with diverging number of predictors
, 2016 This paper concerns the robust regression model when the number of predictors and the number of observations grow in a similar rate. Theory for M-estimators in this regime has been recently developed by several authors [El Karoui et al., 2013, Bean et al.
Nevo, Daniel, Ritov, Ya'acov
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