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Found 21 papers, 6 papers with code
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals.
High-Dimensional False Discovery Rate Control for Dependent Variables
In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples.
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking
In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR).
Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Learning a graph from data is the key to taking advantage of graph signal processing tools.
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications
By probing the theoretical underpinnings linking the BCA and MM algorithmic frameworks, we reveal the direct correlations between the equivalent transformation techniques, essential to the development of WMMSE and WSR-FP, and the surrogate functions pivotal to WSR-MM.
A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization
The mean and variance of portfolio returns are the standard quantities to measure the expected return and risk of a portfolio.
Adaptive Estimation of Graphical Models under Total Positivity
We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models.
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution
Initially, profit and risk were measured by the first two moments of the portfolio's return, a. k. a.
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables.
Solving High-Order Portfolios via Successive Convex Approximation Algorithms
Investors pursue higher mean and lower variance when designing the portfolios.
Does the $\ell_1$-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
We propose a numerical algorithm based on based on the alternating direction method of multipliers, and establish its theoretical sequence convergence.
Learning Undirected Graphs in Financial Markets
We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data.
M-estimators of scatter with eigenvalue shrinkage
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean.
Structured Graph Learning Via Laplacian Spectral Constraints
Then we introduce a unified graph learning framework, lying at the integration of the spectral properties of the Laplacian matrix with Gaussian graphical modeling that is capable of learning structures of a large class of graph families.
Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I
Different from a number of existing approaches, however, the proposed framework is flexible enough to incorporate a class of non-convex objective functions, allow distributed operation with and without a fusion center, and include variance reduced methods as special cases.
Sparse Reduced Rank Regression With Nonconvex Regularization
Numerical simulations show that the proposed algorithm is much more efficient compared to the benchmark methods and the nonconvex function can result in a better estimation accuracy.
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model
In econometrics and finance, the vector error correction model (VECM) is an important time series model for cointegration analysis, which is used to estimate the long-run equilibrium variable relationships.
Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation
In addition, we propose a method to improve the covariance estimation problem when its underlying eigenvectors are known to be sparse.
Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions
This paper considers the problem of robustly estimating a structured covariance matrix with an elliptical underlying distribution with known mean.
Sparse Generalized Eigenvalue Problem via Smooth Optimization
Then an algorithm is developed via iteratively majorizing the surrogate function by a quadratic separable function, which at each iteration reduces to a regular generalized eigenvalue problem.
