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Journal ArticleDOI

Coherent mixtures of SRSWOR sampling schemes for bounded risk estimation of a finite population mean

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TLDR
In this article, the problem of characterization of coherent sequential sampling schemes in the context of bounded risk estimation of a finite population mean has been studied and some solutions to the problem have been provided.
Abstract
In a recent study(Mukhopadhyay et al (1981), Sinha (1989)) a problem on characterization of coherent sequential sampling schemes arose in the context of bounded risk estimation of a finite population mean. In this paper we provide some solutions to the problem after properly formulating the same.

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Citations
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Efficient sequential sampling strategies for environmental monitoring

TL;DR: In this article, a fixed-precision sampling procedure for environmental monitoring is proposed, where each resource is assigned a population inclusion probability, with the intent of describing the distribution of the whole population of resources at risk to anthropogenic environmental stresses.
References
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Journal ArticleDOI

Some Remarks on Sampling with Replacement

TL;DR: In this paper, it was shown that the bias is numerically less if the ratio estimate is based on distinct units regardless of whether these are fixed in advance or considered as random variables.
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Estimating the Mean of a Finite Population

TL;DR: In this article, the authors considered the problem of unbiased linear estimation of the mean of a given value of the value of ε with minimum variance and obtained an admissible estimator, together with a complete class of estimators.
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On Balanced Sampling Schemes

TL;DR: Balanced sampling schemes have been introduced by Roy and Chakravarti (1960) in the search for best regular estimators of the finite population means as discussed by the authors, and they have introduced the regular estimato1·s as those having variances proportional to the population variance (of the study variable).
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Bounded risk estimation of a finite population mean optimal strategies

TL;DR: In this paper, the authors considered three simple random sampling strategies: (i) with replacement, mean per unit estimation, (ii) without replacement and (iii) mean per distinct unit estimation and showed that the second strategy still fares better than the first, although the third strategy may not perform better than second one.