Title Penalized Blind Kriging in Computer Experiments Complete List of Authors Ying Hung Penalized Blind Kriging in Computer Experiments

Kriging models are popular in analyzing computer experiments. The most widely used kriging models apply a constant mean to capture the overall trend. This method can lead to a poor prediction especially when certain strong trends exist. To tackle this problem, a new modeling method is proposed, which incorporates a variable selection mechanism into kriging via a penalty function. An efficient algorithm is introduced and the oracle properties in terms of selecting the correct mean function are derived according to the fixed-domain asymptotics. The finite-sample performance is examined via a simulation study. Application of the proposed methodology to circuit-simulation experiments demonstrates a remarkable improvement in prediction and the capability of identifying variables that most affect the system.