A Majorization-Minimization gLASSO Framework for SETAR Models: Theory, Simulation, and Application to PM 2.5 Data

Empirical likelihood inference for a unified mixed INAR(1) model

Generalized multivariate threshold autoregressive models with linearly partitioned threshold space

We consider a k-dimensional multiple-regime vector threshold autoregressive model, in which the regime-switching mechanism is governed by a bivariate threshold variable. Specifically, the regimes are induced by a partition of the threshold space by an unknown number of threshold lines. Within each regime, the process f...

Model-agnostic noise reduction for high-dimensional time series data

Simulations show that the proposed model-agnostic framework for noise reduction in high-dimensional time series can substantially improve subspace estimation, reconstruction error, and one-step-ahead forecast accuracy compared with both orthogonal projection-based denoising and the raw data.

Full Bayesian Analysis of ARX Models Under Scale-Mixtures of Normal Errors: An Application to Solar Radiation in Najran, Saudi Arabia

Autoregressive models with exogenous variables (ARX) constitute a fundamental family of time series tools with broad applicability across engineering, environmental science, and finance. A persistent limitation of standard Bayesian treatments is the Gaussian error assumption, which frequently proves inadequate when dea...

Scalable estimation of VARMA models

It is proved that both recover the infinite-autoregressive representation of the true process at a near-parametric rate in fixed dimension, so the truncation introduces no asymptotic bias.

A generic nonparametric value-at-risk estimator for high dimensions

We present in this article a non-parametric value-at-risk (VaR+CVaR) algorithm that remains accurate for an arbitrarily large number of underlying positions. The algorithm solves the two inherent problems of VaR estimation. First, past history is not directly applicable to the future, but all predictions of the future...