High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood

Kimmo Suotsalo; Yingying Xu; Jukka Corander; Johan Pensar

doi:10.1007/s11222-021-10049-z

ScienceGate Book Chapters

JOURNAL ARTICLE

High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood

Kimmo Suotsalo Yingying Xu Jukka Corander Johan Pensar

Year: 2021 Journal: Statistics and Computing Vol: 31 (6) Publisher: Springer Science+Business Media

DOI: 10.1007/s11222-021-10049-z

Get Full-Text PDF Get Analytical Report

Keywords:

Autoregressive model Marginal likelihood Mathematics Multivariate statistics Estimator Consistency (knowledge bases) Bayesian probability Computer science Artificial intelligence Pattern recognition (psychology) Algorithm Statistics

Metrics

Cited By

0.78

FWCI (Field Weighted Citation Impact)

Refs

0.70

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood

Metrics

Citation History

Topics

Related Documents

Learning Gaussian graphical models with fractional marginal pseudo-likelihood

Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-Likelihood

Structure Learning of Contextual Markov Networks using Marginal Pseudo‐likelihood

Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models

Bayesian Nonparametric Sparse Vector Autoregressive Models