A theory of truncated inverse sampling

In this paper, we have established a new theory of truncated inverse sampling for estimating mean values of non-negative random variables such as binomial, Poisson, hyper-geometrical, and bounded variables. We have derived explicit formulas and computational methods for designing sampling schemes to...

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Bibliographic Details
Published inSequential analysis Vol. 37; no. 4; pp. 455 - 486
Main Author Chen, Xinjia
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.10.2018
Taylor & Francis Ltd
Subjects
Bounded random variable
confidence interval
Economic models
Estimating techniques
Monte Carlo estimation
nonasymptotic method
Random variables
Random walk
Sampling methods
sequential estimation
Truncated inverse sampling
Uncertainty
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Summary:In this paper, we have established a new theory of truncated inverse sampling for estimating mean values of non-negative random variables such as binomial, Poisson, hyper-geometrical, and bounded variables. We have derived explicit formulas and computational methods for designing sampling schemes to ensure prescribed levels of precision and confidence for point estimators. Moreover, we have developed interval estimation methods following truncated inverse sampling procedures.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0747-4946
1532-4176
DOI:10.1080/07474946.2018.1554888