A Path Algorithm for the Fused Lasso Signal Approximator 论文

2010Journal of Computational and Graphical Statistics引用 224
DOI
Statistical Methods and InferenceAdvanced Causal Inference TechniquesBayesian Methods and Mixture Models
Statistical Methods and InferenceBayesian Methods and Mixture ModelsAdvanced Causal Inference Techniques
相关技术:Bayesian Methods and Mixture ModelsStatistical Methods and InferenceAdvanced Causal Inference Techniques
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Abstract The Lasso is a very well-known penalized regression model, which adds an L1 penalty with parameter λ1 on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L1 penalty with parameter λ2 on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ1 and λ2. We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article. Keywords: : Convex optimizationLassoPenalized regression

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Hölger Hoefling

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Bayesian Methods and Mixture ModelsStatistical Methods and InferenceAdvanced Causal Inference Techniques

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