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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 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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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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A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems
Entropy, 2020 Artificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well known for its versatility. The selection of decision variables to update is purely stochastic, incurring several issues to the local search capability of the ABC.
Marco Antonio Florenzano Mollinetti +3 more
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Application of variable selection and dimension reduction on predictors of MSE’s development
Journal of Big Data, 2019 Nature create variables using its character component, and variables are sharing characters from a vary small to relatively large scale. This results, variables to have from a vary different to a more similar character, and leads to have a relation ship.
Habtamu Tilaye Wubetie
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Variable selection in multivariate multiple regression.
PLoS ONE, 2020 IntroductionIn many practical situations, we are interested in the effect of covariates on correlated multiple responses. In this paper, we focus on estimation and variable selection in multi-response multiple regression models.
Asokan Mulayath Variyath, Anita Brobbey
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Group Variable Selection Methods with Quantile Regression: A Simulation Study. [PDF]
المجلة العراقية للعلوم الاحصائيةIn many cases, covariates have a grouping structure that can be used in the analysis to identify important groups and the significant members of those groups. This paper reviews some group variable selection methods that utilize quantile regression.
Hussein Hashem
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