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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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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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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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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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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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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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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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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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High-dimensional sign-constrained feature selection and grou**
In this paper, we propose a non-negative feature selection/feature grou** (nnFSG) method for general sign-constrained high-dimensional regression...
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Communication-efficient distributed estimation for high-dimensional large-scale linear regression
In the Master-Worker distributed structure, this paper provides a regularized gradient-enhanced loss (GEL) function based on the high-dimensional...
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Advanced algorithms for penalized quantile and composite quantile regression
In this paper, we discuss a family of robust, high-dimensional regression models for quantile and composite quantile regression, both with and...
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Research on Intelligent Adjustment Technology of Active Reflector of Radio Telescope
With the development of various technologies, the demand for the accuracy of celestial electromagnetic reception of radio telescops has been...
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Group penalized quantile regression
Quantile regression models have become a widely used statistical tool in genetics and in the omics fields because they can provide a rich description...
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Artificial Neural Networks Generated by Low Discrepancy Sequences
Artificial neural networks can be represented by paths. Generated as random walks on a dense network graph, we find that the resulting sparse...
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Sparse Reduced-Rank Regression
Under the high-dimensional multivariate regression framework in chapter 10 , researchers have considered...
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A Riemannian geometric framework for manifold learning of non-Euclidean data
A growing number of problems in data analysis and classification involve data that are non-Euclidean. For such problems, a naive application of...
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The MELODIC Family for Simultaneous Binary Logistic Regression in a Reduced Space
Logistic regression is a commonly used method for binary classification. Researchers often have more than a single binary response variable and...
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High-Dimensional Mediation Analysis with Applications to Causal Gene Identification
Mediation analysis has been a popular framework for elucidating the mediating mechanism of the exposure effect on the outcome in many disciplines...
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The conditional censored graphical lasso estimator
In many applied fields, such as genomics, different types of data are collected on the same system, and it is not uncommon that some of these...
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Predictive stability criteria for penalty selection in linear models
Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data....
