Penalized blind kriging in computer experiments
Ying Hung · Statistica Sinica · 2011
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 when strong trends exist. To tackle this problem, a new modeling method is proposed, which incorporates a vari- able selection mechanism into kriging via a penalty function. An efficient algorithm is introduced and oracle properties in terms of selecting the correct mean function are derived according to 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 predic- tion, and the capability of identifying variables that most affect the system.