3 accepted papers
The vector autoregression (VAR) is widely used to model dynamical systems. As the model dimension increases, the reduced-rank (RR) VAR modelling effectively reduces the number of parameters significantly by factorization and mitigates the risk of overfitting. However, the crucial problem in identifi…
The first order vector autoregression with a symmetric transition matrix (sym-VAR(1)) occurs widely in applications such as electrical networks, robotics and mechanical systems. However, there is so far almost no work on estimating a sym- VAR(1) from time series data that guarantees the estimated sy…
Point processes are finding increasing applications in neuroscience, genomics, and social media. But basic modelling properties are little studied. Here we consider a periodic time-varying Poisson model and develop the asymptotic Cramer-Rao bound. We also develop, for the first time, a maximum likel…