• Bayesian change-point detection on multi-source temporal sequences of observations with mixed statistical type and high dimension Local observation models for support alignment adaptive methodology Explainability the contribution each source to global Real-world experiments smartphone-based monitoring dataset healthcare In this work we address problem (CPD) high-dimensional, multi-source, het...

We study the detection of a sparse change in high-dimensional mean vector as minimax testing problem. Our first main contribution is to derive exact rate across all parameter regimes for n independent, p-variate Gaussian observations. This exhibits phase transition when sparsity level order ploglog(8n) and has very delicate dependence on sample size: certain regime, it involves triple iterated ...

Abstract. As the majority of earth population is living in urban environments, cities are continuously evolving and efficient monitoring tools needed to retrieve classify their evolution. In this context, analysing changes between two dates a crucial point. most occur along vertical axis (with new construction or demolition buildings) use 3D data therefore mandatory. Among them, LiDAR constitut...

Abstract A method for change point detection is proposed. We consider a univariate sequence of independent random variables with piecewise constant expectation and variance, apart from which the distribution may vary periodically. aim to detect points in both variance. For that, we propose statistical test null hypothesis no an algorithm detection. Both are based on bivariate moving sum approac...