A MISSPECIFICATION TEST FOR SIMULATION METAMODELS

Shiyu Wang; Guangwu Liu; Kun Zhang

doi:10.1109/WSC.2017.8247929

A MISSPECIFICATION TEST FOR SIMULATION METAMODELS

College of Business

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of √n, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.

Original language	English
Title of host publication	Proceedings of the 2017 Winter Simulation Conference
Publisher	IEEE
Pages	1938-1949
ISBN (Electronic)	978-1-5386-3428-8
ISBN (Print)	978-1-5386-3430-1
DOIs	https://doi.org/10.1109/WSC.2017.8247929
Publication status	Published - Dec 2017
Event	2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States Duration: 3 Dec 2017 → 6 Dec 2017 http://meetings2.informs.org/wordpress/wsc2017/

Publication series

Name	Proceedings - Winter Simulation Conference
ISSN (Print)	0891-7736
ISSN (Electronic)	1558-4305

Conference

Conference	2017 Winter Simulation Conference, WSC 2017
Abbreviated title	WSC 2017
Place	United States
City	Las Vegas
Period	3/12/17 → 6/12/17
Internet address	http://meetings2.informs.org/wordpress/wsc2017/

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@inproceedings{c787508d534b45c8866c6e027f51b5ca,

title = "A MISSPECIFICATION TEST FOR SIMULATION METAMODELS",

abstract = "In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of √n, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.",

author = "Shiyu Wang and Guangwu Liu and Kun Zhang",

year = "2017",

month = dec,

doi = "10.1109/WSC.2017.8247929",

language = "English",

isbn = "978-1-5386-3430-1",

series = "Proceedings - Winter Simulation Conference",

publisher = "IEEE",

pages = "1938--1949",

booktitle = "Proceedings of the 2017 Winter Simulation Conference",

address = "United States",

note = "2017 Winter Simulation Conference, WSC 2017, WSC 2017 ; Conference date: 03-12-2017 Through 06-12-2017",

url = "http://meetings2.informs.org/wordpress/wsc2017/",

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T1 - A MISSPECIFICATION TEST FOR SIMULATION METAMODELS

AU - Wang, Shiyu

AU - Liu, Guangwu

AU - Zhang, Kun

PY - 2017/12

Y1 - 2017/12

N2 - In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of √n, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.

AB - In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of √n, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.

UR - https://www.scopus.com/pages/publications/85044505547

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85044505547&origin=recordpage

U2 - 10.1109/WSC.2017.8247929

DO - 10.1109/WSC.2017.8247929

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-5386-3430-1

T3 - Proceedings - Winter Simulation Conference

SP - 1938

EP - 1949

BT - Proceedings of the 2017 Winter Simulation Conference

PB - IEEE

T2 - 2017 Winter Simulation Conference, WSC 2017

Y2 - 3 December 2017 through 6 December 2017

ER -

A MISSPECIFICATION TEST FOR SIMULATION METAMODELS

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A MISSPECIFICATION TEST FOR SIMULATION METAMODELS

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