Journal ArticleDOI
A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions
TLDR
With the theoretical and empirical evidence, it is shown that the proposed estimator is better than its competitors in statistical accuracy and has clear computational advantages.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2021-03-01. It has received 8 citations till now. The article focuses on the topics: Estimator & Linear discriminant analysis.read more
Citations
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Journal ArticleDOI
Early detection of cardiovascular autonomic neuropathy: A multi-class classification model based on feature selection and deep learning feature fusion
Ted Fleming,Rafiul Hassan,Shamsul Huda,Mohammad Mehedi Hassan,Jemal H. Abawajy,Ahmed Alsanad,Giancarlo Fortino +6 more
TL;DR: The proposed classification algorithm develops a multistage fusion model by combining feature selection and multimodal feature fusion techniques to guarantee highly significant features of CAN and significantly improved the diagnostic accuracy of CAN compared to conventional Ewing battery features.
Journal ArticleDOI
Persistent-homology-based machine learning: a survey and a comparative study
Journal ArticleDOI
Resolution of Degeneracy in Merton's Portfolio Problem
Chi Seng Pun,Hoi Ying Wong +1 more
TL;DR: This study proposes a constrained $\ell_1$-minimization approach to resolve the degeneracy in the high-dimensionalSetting and stabilize the performance in the low-dimensional setting and proves the consistency of the framework that the estimate of the optimal control tends to be the optimal value.
Journal ArticleDOI
Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection
TL;DR: A new architecture of GAN is presented and it is adapted to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment) and the new architecture is termed GANr.
Journal ArticleDOI
A Sparse Learning Approach to Relative-Volatility-Managed Portfolio Selection
TL;DR: In this paper, a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, is proposed, and investigated its application to fina...
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Journal ArticleDOI
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Amir Beck,Marc Teboulle +1 more
TL;DR: A new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global rate of convergence which is proven to be significantly better, both theoretically and practically.
Journal ArticleDOI
Sparse inverse covariance estimation with the graphical lasso
TL;DR: Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods.
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