The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the system. Due large size networks poor coverage measurement instrumentation on field, designing fast algorithms achieving accurate results are two major challenges associated system estimation. In this paper, an efficient solution for performing m...

Change point estimation is as an effective method for identifying the time of a change in production and service processes. In most of the statistical quality control literature, it is usually assumed that the quality characteristic of interest is independently and identically distributed over time. It is obvious that this assumption could be easily violated in practice. In this paper, we use m...

Energy efficiency, low latency, high estimation accuracy, and fast convergence are important goals in distributed incremental estimation algorithms for sensor networks. One approach that adds flexibility in achieving these goals is clustering. In this paper, the framework of distributed incremental estimation is extended by allowing clustering amongst the nodes. Among the observations made is t...

Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a number of combination methods, and provide both theoretical and experimental ana...