no code implementations • 3 Apr 2024 • Andrei Buciulea, Jiaxi Ying, Antonio G. Marques, Daniel P. Palomar
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals.
no code implementations • 28 Jan 2024 • Jasin Machkour, Michael Muma, Daniel P. Palomar
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.
no code implementations • 26 Jan 2024 • Jasin Machkour, Daniel P. Palomar, Michael Muma
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).
no code implementations • 16 Jan 2024 • Jasin Machkour, Arnaud Breloy, Michael Muma, Daniel P. Palomar, Frédéric Pascal
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
no code implementations • 28 Dec 2023 • Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
Learning a graph from data is the key to taking advantage of graph signal processing tools.
1 code implementation • 8 Nov 2023 • Zepeng Zhang, Ziping Zhao, Kaiming Shen, Daniel P. Palomar, Wei Yu
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.
no code implementations • 14 Dec 2022 • Shengjie Xiu, Xiwen Wang, Daniel P. Palomar
The mean and variance of portfolio returns are the standard quantities to measure the expected return and risk of a portfolio.
no code implementations • 27 Oct 2022 • Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar
We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models.
1 code implementation • 6 Jun 2022 • Xiwen Wang, Rui Zhou, Jiaxi Ying, Daniel P. Palomar
Initially, profit and risk were measured by the first two moments of the portfolio's return, a. k. a.
no code implementations • 12 Oct 2021 • Jasin Machkour, Michael Muma, Daniel P. Palomar
The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables.
1 code implementation • 3 Aug 2020 • Rui Zhou, Daniel P. Palomar
Investors pursue higher mean and lower variance when designing the portfolios.
1 code implementation • 26 Jun 2020 • Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar
We propose a numerical algorithm based on based on the alternating direction method of multipliers, and establish its theoretical sequence convergence.
no code implementations • 20 May 2020 • José Vinícius de Miranda Cardoso, Daniel P. Palomar
We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data.
no code implementations • 12 Feb 2020 • Esa Ollila, Daniel P. Palomar, Frederic Pascal
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.
2 code implementations • NeurIPS 2019 • Sandeep Kumar, Jiaxi Ying, Jos'e Vin'icius de M. Cardoso, Daniel P. Palomar
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.
no code implementations • 21 Jul 2019 • Sandeep Kumar, Ketan Rajawat, Daniel P. Palomar
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.
no code implementations • 20 Mar 2018 • Ziping Zhao, Daniel P. Palomar
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.
no code implementations • 16 Oct 2017 • Ziping Zhao, Daniel P. Palomar
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.
no code implementations • 12 Feb 2016 • Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar
In addition, we propose a method to improve the covariance estimation problem when its underlying eigenvectors are known to be sparse.
no code implementations • 17 Jun 2015 • Ying Sun, Prabhu Babu, Daniel P. Palomar
This paper considers the problem of robustly estimating a structured covariance matrix with an elliptical underlying distribution with known mean.
1 code implementation • 28 Aug 2014 • Junxiao Song, Prabhu Babu, Daniel P. Palomar
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.