Principal component-guided sparse regression
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This article is published in Canadian Journal of Statistics-revue Canadienne De Statistique.The article was published on 2021-04-16 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Lasso (statistics) & Principal component analysis.read more
Citations
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Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions
TL;DR: The Functional-Hybrid model as discussed by the authors uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models for the representation of (bio)-chemical processes, focusing on applying chemical reaction kinetic principles to classical chemical reactions, biochemistry, ecology, physiology and a bioreactor.
Journal ArticleDOI
Functional-Hybrid Modeling through automated adaptive symbolic regression for interpretable mathematical expressions
TL;DR: The Functional-Hybrid model as discussed by the authors uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models for the representation of (bio)-chemical processes, focusing on applying chemical reaction kinetic principles to classical chemical reactions, biochemistry, ecology, physiology and a bioreactor.
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OUP accepted manuscript
TL;DR: SuffPCR as discussed by the authors first estimates sparse principal components and then estimates a linear model on the recovered subspace, which yields improved predictions in high-dimensional tasks including regression and classification, especially in the typical context of omics with correlated features.
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Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms
TL;DR: In this paper , the authors proposed a primal-dual algorithm for minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of F with a linear operator L. This problem has many applications, for instance, in image processing and machine learning.
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An analytical shrinkage estimator for linear regression
TL;DR: This article derived an analytical solution to the optimal shrinkage of OLS regression coefficients toward a constant target, under any first two moments of predictors, which closely mimics the prediction performance of ridge penalty, which admits no general analytical solution.
References
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Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
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Regularization and variable selection via the elastic net
Hui Zou,Trevor Hastie +1 more
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
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Regularization Paths for Generalized Linear Models via Coordinate Descent
TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.
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Model selection and estimation in regression with grouped variables
Ming Yuan,Yi Lin +1 more
TL;DR: In this paper, instead of selecting factors by stepwise backward elimination, the authors focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection.