Results 11 to 20 of about 4,611 (130)
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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Regularized brain reading with shrinkage and smoothing [PDF]
, 2016 Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare
Ramdas, Aaditya +3 more
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Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression [PDF]
, 2007 Main objectives of feature extraction in signal regression are the improvement of accuracy of prediction on future data and identification of relevant parts of the signal. A feature extraction procedure is proposed that uses boosting techniques to select
Gertheiss, Jan, Tutz, Gerhard
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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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, 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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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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Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]
, 2008 During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Fahrmeir, Ludwig +2 more
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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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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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Boosting Correlation Based Penalization in Generalized Linear Models [PDF]
, 2007 In high dimensional regression problems penalization techniques are a useful tool for estimation and variable selection. We propose a novel penalization technique that aims at the grouping effect which encourages strongly correlated predictors to be in ...
Tutz, Gerhard, Ulbricht, Jan
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