A simulation-based estimation method for bias reduction

Jin Fang and L. Jeff Hong

IISE Transactions, 2018, vol. 50, issue 1, 14-26

Abstract: Models are often built to evaluate system performance measures or to make quantitative decisions. These models sometimes involve unknown input parameters that need to be estimated statistically using data. In these situations, a statistical method is typically used to estimate these input parameters and the estimates are then plugged into the models to evaluate system output performances. The output performance estimators obtained from this approach usually have large bias when the model is nonlinear and the sample size of the data is finite. A simulation-based estimation method to reduce the bias of performance estimators for models that have a closed-form expression already exists in the literature. In this article, we extend that method to more general situations where the models have no closed-form expression and can only be evaluated through simulation. A stochastic root-finding problem is formulated to obtain the simulation-based estimators and several algorithms are designed. Furthermore, we give a thorough asymptotic analysis of the properties of the simulation-based estimators, including the consistency, the order of the bias, the asymptotic variance, and so on. Our numerical experiments show that the experimental results are consistent with the theoretical analysis.

Date: 2018
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2017.1382751 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:50:y:2018:i:1:p:14-26

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2017.1382751

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().