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Found 6 papers, 1 papers with code
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA
Sparse principal component analysis (PCA) and sparse canonical correlation analysis (CCA) are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data.
Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models
The Tweedie GLM is a widely used method for predicting insurance premiums.
Methodology
Another Look at DWD: Thrifty Algorithm and Bayes Risk Consistency in RKHS
We propose a novel efficient algorithm for solving DWD, and our algorithm can be several hundred times faster than the existing state-of-the-art algorithm based on the SOCP.
Sparse Distance Weighted Discrimination
Distance weighted discrimination (DWD) was originally proposed to handle the data piling issue in the support vector machine.
High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Theoretically, the proposed methods achieve the same rates of convergence for both precision matrix estimation and eigenvector estimation, as if the latent variables were observed.
Strong oracle optimality of folded concave penalized estimation
Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation.
