منابع مشابه
Fused Lasso Additive Model.
We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a...
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In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer’s disease using neuroimaging data. Specifically, we may wish to estimate a brain network for the normal controls (NC), a brain network for the...
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In the last decade several estimators have been proposed that enforce the grouping property. A regularized estimate exhibits the grouping property if it selects groups of highly correlated predictor rather than selecting one representative. The pairwise fused lasso is related to fusion methods but does not assume that predictors have to be ordered. By penalizing parameters and differences betwe...
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As a flexible alternative to the Cox model, the additive risk model assumes that the hazard function is the sum of the baseline hazard and a regression function of covariates. For right censored survival data when variable selection is needed along with model estimation, we propose a path consistent model selector using a modified Lasso approach, under the additive risk model assumption. We sho...
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We consider estimating an unknown signal, which is both blocky and sparse, corrupted by additive noise. We study three interrelated least squares procedures and their asymptotic properties. The first procedure is the fused lasso, put forward by Friedman et al. (2007), which we modify into a different estimator, called the fused adaptive lasso, with better properties. The other two estimators we...
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2016
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2015.1073155