Results 11 to 20 of about 3,737,276 (277)
Adaptive robust variable selection
The Annals of Statistics, 2014 Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted $L_1$-penalty, called weighted robust Lasso (WR-Lasso), in which weights are introduced to ameliorate the ...
Fan, Jianqing +2 more
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High-dimensional variable selection
The Annals of Statistics, 2009 This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models.
Wasserman, Larry, Roeder, Kathryn
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Systematic Review of Variable Selection Bias in Species Distribution Models for Aedes vexans (Diptera: Culicidae) [PDF]
InsectsWe conducted a systematic literature review, following PRISMA guidelines, to assess whether existing species distribution models for Aedes vexans reflect its known ecological requirements.
Peter Pothmann +3 more
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Variable selection methods for descriptive modeling [PDF]
PLoS ONEA. D. V. Tharkeshi T. Dharmaratne +3 more
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Variable selection for decentralized control [PDF]
Modeling, Identification and Control, 1992 Decentralized controllers (single-loop controllers applied to multivariable plants) are often preferred in practice because they are robust and relatively simple to understand and to change. The design of such a control system starts with pairing inputs (
Sigurd Skogestad, Manfred Morari
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Stable Iterative Variable Selection [PDF]
Bioinformatics, 2021 AbstractMotivationThe emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature
Mehrad Mahmoudian +3 more
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Gibbs Variable Selection Using BUGS
Journal of Statistical Software, 2002 In this paper we discuss and present in detail the implementation of Gibbs variable selection as defined by Dellaportas et al. (2000, 2002) using the BUGS software (Spiegelhalter et al. , 1996a,b,c).
Ioannis Ntzoufras
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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 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 using MM algorithms [PDF]
, 2005 Variable selection is fundamental to high-dimensional statistical modeling. Many variable selection techniques may be implemented by maximum penalized likelihood using various penalty functions.
Hunter, David R., Li, Runze
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