Dynamic Chain Graph Models for Ordinal Time Series Data

TL;DR

This paper presents a novel sparse dynamic chain graph model for high-dimensional non-Gaussian time series data, enabling network inference with efficient estimation algorithms and applications to simulated and genomic datasets.

Contribution

Introduces a new Gaussian copula vector autoregressive model with a penalized EM algorithm for sparse network inference in high-dimensional ordinal time series.

Findings

Effective in modeling complex dependencies in high-dimensional data

Demonstrated on simulated datasets showing accurate network recovery

Applied to genomic data revealing meaningful interactions

Abstract

This paper introduces sparse dynamic chain graph models for network inference in high dimensional non-Gaussian time series data. The proposed method parametrized by a precision matrix that encodes the intra time-slice conditional independence among variables at a fixed time point, and an autoregressive coefficient that contains dynamic conditional independences interactions among time series components across consecutive time steps. The proposed model is a Gaussian copula vector autoregressive model, which is used to model sparse interactions in a high-dimensional setting. Estimation is achieved via a penalized EM algorithm. In this paper, we use an efficient coordinate descent algorithm to optimize the penalized log-likelihood with the smoothly clipped absolute deviation penalty. We demonstrate our approach on simulated and genomic datasets. The method is implemented in an R package tsnetwork.