Minimum Density Power Divergence Research Articles

Robust estimation for bivariate integer-valued autoregressive models based on minimum density power divergence

This study considers a robust estimation method for bivariate integer-valued autoregressive (BINAR) models using a minimum density power divergence (MDPD) scheme. We first address this problem within a general framework of time series models to verify the strong consistency and asymptotic normality of the MDPD estimator (MDPDE) under regularity conditions. Based on these results, we assert the same asymptotic properties of MDPDE for BINAR models and propose an outlier detection rule based on MDPDE. To assess the performance of the proposed method, we conduct a simulation study. Real data analysis is also carried out for illustration, using the number of monthly earthquakes in the United States.

Robust test for structural instability in dynamic factor models

In this paper, we consider a robust test for structural breaks in dynamic factor models. The proposed framework considers structural changes when the underlying high-dimensional time series is contaminated by outlying observations, which are often observed in many real applications such as fMRI, economics and finance. We propose a test based on the robust estimation of a vector autoregressive model for principal component factors using minimum density power divergence. The simulations study shows excellent finite sample performance, higher powers while achieving good sizes in all cases considered. Our method is illustrated to the resting state fMRI series to detect brain connectivity changes.