Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models

Reduced-rank Vector Generalized Linear Models

Change point estimation in high dimensional Markov random-field models.

This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logari...

Change-Point Estimation in High Dimensional Regression Models

We consider high dimensional nonhomogeneous linear regression models with p n 9 0 or p >> n, where p is the number of features and n is the number of observations. In the model considered, the underlying true regression coefficients undergo multiple changes. Our goal is to estimate the number and locations of these change-points and estimate sparse coefficients in each of the intervals between ...

Bootstrap procedures for sequential change point analysis in autoregressive models

We compare numerically the behavior of several bootstrap procedures for monitoring changes in the error distribution of autoregressive time series. The proposed procedures include classical approaches based on the empirical distribution function as well as Fourier-type methods which utilize the empirical characteristic function, both functions being computed on the basis of properly estimated r...

Vector Autoregressive Models