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A sparsity preserving stochastic gradient methods for sparse regression

Lin, Qihang; Chen, Xi; Peña, Javier - In: Computational Optimization and Applications 58 (2014) 2, pp. 455-482

We propose a new stochastic first-order algorithm for solving sparse regression problems. In each iteration, our …

Persistent link: https://www.econbiz.de/10010998369

Inference on treatment effects after selection among high-dimensional controls

Belloni, Alexandre; Chernozhukov, Victor; Hansen, … - In: The review of economic studies 81 (2014) 2, pp. 608-650

Persistent link: https://www.econbiz.de/10010485099

Tuning parameter selection in sparse regression modeling

Hirose, Kei; Tateishi, Shohei; Konishi, Sadanori - In: Computational Statistics & Data Analysis 59 (2013) C, pp. 28-40

In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of …

Persistent link: https://www.econbiz.de/10010595080

A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers

Lucas, Joseph; Carvalho, Carlos; West, Mike - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 11-11

We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following...

Persistent link: https://www.econbiz.de/10005246499