Coordinate Descent Methods for the Penalized Semiparametric Additive Hazards Model
نویسندگان
چکیده
منابع مشابه
Penalized Bregman Divergence Estimation via Coordinate Descent
Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores...
متن کاملCoordinate Descent Algorithms for Lasso Penalized Regression
Imposition of a lasso penalty shrinks parameter estimates toward zero and performs continuous model selection. Lasso penalized regression is capable of handling linear regression problems where the number of predictors far exceeds the number of cases. This paper tests two exceptionally fast algorithms for estimating regression coefficients with a lasso penalty. The previously known ℓ2 algorithm...
متن کاملRegularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ℓ1 and ℓ2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a regularization path. We demonstrate the efficacy of our algorithm on real and simulated data sets, and find considerable speedup between our algori...
متن کاملA Coordinate Majorization Descent Algorithm for l1 Penalized Learning
The glmnet package by [1] is an extremely fast implementation of the standard coordinate descent algorithm for solving l1 penalized learning problems. In this paper, we consider a family of coordinate majorization descent algorithms for solving the l1 penalized learning problems by replacing each coordinate descent step with a coordinate-wise majorization descent operation. Numerical experiment...
متن کاملRescaled Coordinate Descent Methods for Linear Programming
We propose two simple polynomial-time algorithms to find a positive solution to Ax = 0. Both algorithms iterate between coordinate descent steps similar to von Neumann’s algorithm, and rescaling steps. In both cases, either the updating step leads to a substantial decrease in the norm, or we can infer that the condition measure is small and rescale in order to improve the geometry. We also show...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2012
ISSN: 1548-7660
DOI: 10.18637/jss.v047.i09