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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Estimating Time-Varying Graphical Models.

Jilei Yang1, Jie Peng1

  • 1Department of Statistics, University of California, Davis.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|April 8, 2021
PubMed
Summary
This summary is machine-generated.

We introduce LOcal Group Graphical Lasso Estimation (loggle), a new model for understanding how relationships between variables change over time. This method effectively captures gradual graph topology shifts using a novel penalty, aiding in analyzing dynamic systems like stock market interactions.

Keywords:
ADMM algorithmGaussian graphical modelS&P 500group-lassopseudo-likelihood approximation

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Area of Science:

  • Statistics
  • Time Series Analysis
  • Network Science

Background:

  • Understanding evolving relationships among random variables is crucial in many fields.
  • Existing methods may not adequately capture gradual changes in network structures over time.
  • Applications include financial markets, where interdependencies shift dynamically.

Purpose of the Study:

  • To propose a novel model, LOcal Group Graphical Lasso Estimation (loggle), for time-varying graphical models.
  • To develop an efficient algorithm for fitting the loggle model.
  • To demonstrate the utility of loggle in analyzing dynamic network structures.

Main Methods:

  • Developed the loggle model incorporating a local group-lasso penalty for gradual graph topology changes.
  • Implemented an ADMM-based algorithm with blockwise computation and pseudo-likelihood approximation for efficiency.
  • Utilized simulation experiments and real-world S&P 500 stock price data for evaluation.

Main Results:

  • The loggle model effectively incorporates information from neighboring time points, imposing structural smoothness on graphs.
  • The ADMM algorithm provides computational efficiency for fitting the model.
  • Empirical results show loggle can reveal dynamic interacting relationships in financial data.

Conclusions:

  • loggle offers a robust framework for analyzing time-varying graphical models with gradually changing structures.
  • The developed computational methods enhance the practical applicability of such models.
  • The model successfully identified evolving stock price and sector interactions during a financial crisis.