How to estimate time-varying Vector Autoregressive Models? A comparison of two methods

1 code implementation • 14 Nov 2017 • Jonas M. B. Haslbeck, Laura F Bringmann, Lourens J. Waldorp

We compare the performance of stationary and time-varying models, and discuss the theoretical characteristics of all methods in the light of the simulation results.

Applications

Estimating the Number of Clusters via Normalized Cluster Instability

2 code implementations • 26 Aug 2016 • Jonas M. B. Haslbeck, Dirk U. Wulff

We improve current instability-based methods for the selection of the number of clusters $k$ in cluster analysis by developing a normalized cluster instability measure that corrects for the distribution of cluster sizes, a previously unaccounted driver of cluster instability.

MGM: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

4 code implementations • 23 Oct 2015 • Jonas M. B. Haslbeck, Lourens J. Waldorp

We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data.

Applications