Overview
Mikko J. Sillanpää is a statistician (University of Helsinki, later University of Oulu) specializing in Bayesian methods for statistical genetics and gene mapping, where high-dimensional, sparse variable selection is central.
Key Contributions / Features
- Bayesian variable-selection review — O'Hara–Sillanpää (2009): With R.B. O'Hara, reviewed and benchmarked (in BUGS) the main Bayesian variable-selection methods — Kuo–Mallick, GVS, SSVS, adaptive shrinkage, and reversible-jump MCMC — motivated in part by the sparse, leptokurtic effect-size setting of gene mapping. See O'Hara-Sillanpää (2009) and Variable Selection.
Related