Dong Hui scite author profile

Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named journal. INFORMS journal templates are for the exclusive purpose of submitting to an INFORMS journal and should not be used to distribute the papers in print or online or to submit the papers to another publication.

show abstract

Constructing confidence intervals for a quantile using batching and sectioning when applying Latin hypercube sampling

Hui¹,

Nakayama²

2014

View full text Add to dashboard Cite

Quantile estimation using conditional Monte Carlo and Latin hypercube sampling

Hui¹,

Nakayama²

2017

View full text Add to dashboard Cite

A Tutorial on Quantile Estimation via Monte Carlo

Hui

Nakayama

2020

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Dong Hui

Phase separation of grafted polymers under strong demixing interaction

Quantile Estimation with Latin Hypercube Sampling

Constructing confidence intervals for a quantile using batching and sectioning when applying Latin hypercube sampling

Quantile estimation using conditional Monte Carlo and Latin hypercube sampling

A Tutorial on Quantile Estimation via Monte Carlo

Contact Info

Product

Resources

About