The DOI matches its Crossref record.
Detecting Initialization Bias in Simulation Output
Abstract
A general approach to testing for initialization bias in the mean of a simulation output series is presented. The output is transformed into a standardized test sequence that can be contrasted with a known limiting stochastic process. This transformation requires very little computation and the asymptotic theory is applicable to a wide variety of simulations. An initialization bias test is developed and several examples of its application are presented.
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Cited by
The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk
Madras, N., & Sokal, A. D. (1988) · Journal of Statistical Physics · doi:10.1007/bf01022990 · Cited by 939
Joseph, A., Ayyagari, R., Xie, M., Cai, S., Xie, J., Huss, M., & Sikirica, V. (2017) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-017-0962-6 · Cited by 38
Metadata
- Authors
- Schruben, L. W.
- Venue
- Operations Research
- Published
- 1982-06
- Volume
- 30
- Issue
- 3
- Pages
- 569-590
- ISSN
- 0030-364X (ISSN-L)
- 1526-5463
- OpenAlex
- W2018291069
- Cited by
- 235
- References
- 0
- FWCI
- 14.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- Schruben, L. W. (1982). Detecting Initialization Bias in Simulation Output. Operations Research. https://doi.org/10.1287/opre.30.3.569
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
