Nested Heteroscedastic Gaussian Process for Simulation Metamodeling

This paper introduces the nested heteroscedastic Gaussian process approach (NHGP) to tackle simulation metamodeling with large-scale heteroscedastic datasets. NHGP achieves scalability by aggregating sub-stochastic kriging (sub-SK) models built on disjoint subsets of a large-scale dataset, making it user-friendly for SK users. We show that the NHGP predictor possesses desirable statistical properties, including being the best linear unbiased predictor among those built by aggregating sub-SK models and being consistent. The numerical experiments demonstrate the competitive performance of NHGP.