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\footnotesize \noindent Statistica Sinica {\bf 21} (2011), 1171-1190

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\centerline{\large\bf PENALIZED BLIND KRIGING IN COMPUTER} \vskip
2pt \centerline{\large\bf EXPERIMENTS} \vspace{.25cm}
\author{Ying Hung}
\vspace{.4cm} \centerline{\it Rutgers University} \vspace{.55cm}
\begin{quotation}
\noindent {\it Abstract:} 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 variable
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 prediction, and the capability of identifying variables that most
affect the system.\par \vspace{9pt} \noindent {\it Key words and
phrases:} Computer model, fixed-domain asymptotics, Gaussian process
model, geostatistics, oracle procedure, variable selection.
\par
\end{quotation}
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