Results 11 to 20 of about 1,354,633 (198)
Performance of variable selection methods using stability-based selection
BMC Research Notes, 2017 Background Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based ...
Danny Lu +5 more
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Variable Selection of Lasso and Large Model
IEEE Access, 2023 In order to clarify the variable selection of Lasso, Lasso is compared with two other variable selection methods AIC and forward stagewise. First, the variable selection of Lasso was compared with that of AIC, and it was discovered that Lasso has a wider
Huiyi Xia
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Variable selection with Random Forests for missing data [PDF]
, 2013 Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
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Variable selection for the single index model [PDF]
, 2007 We consider variable selection in the single-index model. We prove that the popular leave-m-out crossvalidation method has different behaviour in the single-index model from that in linear regression models or nonparametric regression models.
Yingcun Xia +5 more
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Variable Selection for Spatial Logistic Autoregressive Models
Mathematics, 2022 When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional data
Jiaxuan Liang +4 more
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Variable Selection in ROC Regression [PDF]
Computational and Mathematical Methods in Medicine, 2013 Regression models are introduced into thereceiver operating characteristic(ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable ...
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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas
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Study of Salary Differentials by Gender and Discipline
Statistics and Public Policy, 2017 Although it is 45 years since legislation made gender discrimination on university campuses illegal, salary inequities continue to exist today. The seminal work in studying the existence of salary inequities is that of the American Association of ...
L. Billard
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BMC Medical Research Methodology, 2020 Background Social-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis.
Elizabeth Handorf +3 more
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Stability Selection for Structured Variable Selection
CoRR, 2017 In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives.
George Philipp +2 more
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