In this paper, we provide a review of statistical methods that are useful in conducting
computer experiments. Our focus is on the task of metamodeling, which is driven by the goal …

Experiments have long been used to study the relationship between a set of inputs to a
physical system and the resulting output. Termed physical experiments in this text, there is a …

We consider prediction and uncertainty analysis for systems which are approximated using
complex mathematical models. Such models, implemented as computer codes, are often …

It is more than a decade since Genichi Taguchi's ideas on quality improvement were
inrroduced in the United States. His parameter-design approach for reducing variation in …

Analyzing Stochastic Computer Models: A Review with Opportunities Page 1 Statistical
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …

This paper addresses the difficulty of the previously developed Adaptive Response Surface
Method (ARSM) for high-dimensional design problems. ARSM was developed to search for …

It is argued that estimating the damage costs of a certain benchmark climate change is not
sufficient. What is needed are cost functions and confidence intervals. Although these are …

This chapter and the next discuss how to select inputs at which to compute the output of a
computer experiment to achieve specific goals. The inputs one selects constitute the …

In the field of uncertainty quantification, sparse polynomial chaos (PC) expansions are
commonly used by researchers for a variety of purposes, such as surrogate modeling. Ideas …

In the last ten to fifteen years many phenomena that could be studied only using physical
experiments can now be studied by computer experiments. Advances in the mathematical …