Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets

2016-05-11 · Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique del Castillo

We present a new method for estimating multivariate, second-order stationary Gaussian Random Field (GRF) models based on the Sparse Precision matrix Selection (SPS) algorithm, proposed by Davanloo et al. (2015) for estimating scalar GRF models. Theoretical convergence rates for the estimated between-response covariance matrix and for the estimated parameters of the underlying spatial correlation function are established. Numerical tests using simulated and real datasets validate our theoretical findings. Data segmentation is used to handle large data sets.

📄 PDF Abstract BibTeX arXiv:1605.03267

Code (0)

등록된 구현이 없습니다.

Generalized Additive Model Selection

2015-06-11 · Alexandra Chouldechova, Trevor Hastie

We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive mode…

Additive models model Model Selection

Optimization for L1-Norm Error Fitting via Data Aggregation

2017-03-15 · Young Woong Park

We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function. The proposed algorithm…

regression

Quantum Data Fitting Algorithm for Non-sparse Matrices

2019-07-16 · Guangxi Li, Youle Wang, Yu Luo, Yuan Feng

We propose a quantum data fitting algorithm for non-sparse matrices, which is based on the Quantum Singular Value Estimation (QSVE) subroutine and a novel efficient method for recovering the signs of eigenvalues. Our alg…

Generalized Low Rank Models

2014-10-01 · Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen Boyd

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, cat…

Clustering Denoising Matrix Completion

The Regularization Parameter: Sparse Precision Matrix Estimation

2026-07-07 · Aryan Eftekhari, Daniel Sergio Vega, Ernst-Jan Camiel Wit, Olaf Schenk arxiv

Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data. A recurring challenge is appropriately…