Preliminary study on bolstered error estimation in high-dimensional spaces

Error estimation is fundamental in GSP applications, such as the discovery of biomarkers to classify disease, or the construction of genetic regulatory networks, especially in small sample settings. Braga-Neto and Dougherty proposed a kernel-based technique of error estimation, called bolstered error estimation, which was shown empirically to work well in low-dimensional spaces (Braga-Neto and Dougherty, 2004). We present in this paper preliminary results of a simulation study on how bolstering performs in high-dimensional spaces.