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Coordinate descent algorithm for generalized group fused Lasso
We deal with a model with discrete varying coefficients to consider modeling for heterogeneity and clustering for homogeneity, and estimate the...
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Active-set based block coordinate descent algorithm in group LASSO for self-exciting threshold autoregressive model
Group LASSO (gLASSO) estimator has been recently proposed to estimate thresholds for the self-exciting threshold autoregressive model, and a group...
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Coordinate descent algorithm of generalized fused Lasso logistic regression for multivariate trend filtering
Generalized fused Lasso (GFL) is an extension of fused Lasso and performs multivariate trend filtering based on adjacent information among...
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An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled lasso
The sparse precision matrix plays an essential role in the Gaussian graphical model since a zero off-diagonal element indicates conditional...
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An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units
Large-scale sparse precision matrix estimation has attracted wide interest from the statistics community. The convex partial correlation selection...
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A new active zero set descent algorithm for least absolute deviation with generalized LASSO penalty
A new active zero set descent algorithm is proposed for least absolute deviation (LAD) problems with generalized LASSO penalty. Zero set contains the...
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Refining Invariant Coordinate Selection via Local Projection Pursuit
Invariant coordinate selection (ICS), introduced by Tyler et al. (J. Roy. Stat. Soc. B 71(3):549–592, 2009), is a powerful tool to find potentially...
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A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems
By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)...
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High-dimensional penalized Bernstein support vector classifier
The support vector machine (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. However, the...
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Proximal methods for sparse optimal scoring and discriminant analysis
Linear discriminant analysis (LDA) is a classical method for dimensionality reduction, where discriminant vectors are sought to project data to a...
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Unifying Framework for Accelerated Randomized Methods in Convex Optimization
In this paper, we consider smooth convex optimization problems with simple constraints and inexactness in the oracle information such as value,...
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Censored broken adaptive ridge regression in high-dimension
Broken adaptive ridge (BAR) is a penalized regression method that performs variable selection via a computationally scalable surrogate to
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Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models
This paper introduces the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering....
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Robust Moderately Clipped LASSO for Simultaneous Outlier Detection and Variable Selection
Outlier detection has become an important and challenging issue in high-dimensional data analysis due to the coexistence of data contamination and...
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Stochastic Gradient Schemes
By far the most frequently applied instance of stochastic approximation is the stochastic gradient descent (or ascent) algorithm and its many...
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Penalized polygram regression
We consider a study on regression function estimation over a bounded domain of arbitrary shapes based on triangulation and penalization techniques. A...
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Hierarchical disjoint principal component analysis
Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest...
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Byzantine-resilient decentralized network learning
Decentralized federated learning based on fully normal nodes has drawn attention in modern statistical learning. However, due to data corruption,...
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Black-box optimization on hyper-rectangle using Recursive Modified Pattern Search and application to ROC-based Classification Problem
In statistics, it is common to encounter multi-modal and non-smooth likelihood (or objective function) maximization problems, where the parameters...
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A new double-regularized regression using Liu and lasso regularization
This paper discusses a new estimator that performs simultaneous parameter estimation and variable selection in the scope of penalized regression...
