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  • Search: subject:"High Dimensional Model Selection"
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Year of publication
Subject
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False discovery rate 1 High Dimensional Model Selection 1 High-dimensional model selection 1 Inverse Problems 1 Positive selection rate 1 Prediction 1 Random subspace method 1 Robust Optimization 1 Science 1 Statistics and Numeric Data 1 Variable importance 1
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Online availability
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Free 1 Undetermined 1
Type of publication
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Article 1 Other 1
Language
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English 1 Undetermined 1
Author
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Harrington Jr., Patrick Lloyd 1 Mielniczuk, Jan 1 Teisseyre, Paweł 1
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Computational Statistics & Data Analysis 1
Source
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BASE 1 RePEc 1
Showing 1 - 2 of 2
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Inverse Problems in High Dimensional Stochastic Systems Under Uncertainty.
Harrington Jr., Patrick Lloyd - 2010
Increasingly often, problems in modern medicine, quantitative finance, or social-networking involve tens of thousands of variables that interact with each other and jointly evolve over time. The states of these variables may correspond to the phenotype of a particular individual, the price of a...
Persistent link: https://www.econbiz.de/10009476982
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Using random subspace method for prediction and variable importance assessment in linear regression
Mielniczuk, Jan; Teisseyre, PaweÅ‚ - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 725-742
A random subset method (RSM) with a new weighting scheme is proposed and investigated for linear regression with a large number of features. Weights of variables are defined as averages of squared values of pertaining t-statistics over fitted models with randomly chosen features. It is argued...
Persistent link: https://www.econbiz.de/10011056607
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