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Population proportion

About: Population proportion is a research topic. Over the lifetime, 247 publications have been published within this topic receiving 4099 citations.


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Journal ArticleDOI
TL;DR: One of the most basic objectives in data analysis is to estimate an unknown population value from a sample: in the long term, the confidence interval will contain the population value in a given percentage of the samples.

4 citations

Journal ArticleDOI
TL;DR: Bayesian estimation of rare sensitive attribute using randomized response technique, which includes a rare unrelated attribute is proposed, which depends on the parameters and is robust to priors.
Abstract: Randomized response models have been used to estimate a population proportion of a sensitive attribute A randomized device is typically employed to protect respondent's privacy in a survey In addition, an unrelated question is asked to improve the statistical efficiency In this article, we propose Bayesian estimation of rare sensitive attribute using randomized response technique, which includes a rare unrelated attribute Two cases are considered, the proportion of a rare unrelated attribute is known and unknown A simulation study is conducted to assess the performance of the models using mean absolute error and coverage probability The results show that the performance depends on the parameters and is robust to priors

4 citations

Journal ArticleDOI
TL;DR: This article considers the case in which both classifiers are fallible and proposes asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models and suggests that both perform satisfactorily for small to large sample sizes and are highly recommended.
Abstract: Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partially validated data obtained by the double-sampling process. However, it could happen in practice that such infallible classifier or gold standard does not exist. In this article, we consider the case in which both classifiers are fallible and propose asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models. Our results suggest that both asymptotic and approximate unconditional procedures based on the score statistic perform satisfactorily for small to large sample sizes and are highly recommended. When sample size is moderate or large, asymptotic procedures based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic, log- and logit-transformation statistics based on both models generally perform well and are hence recommended. The approximate unconditional procedures based on the log-transformation statistic under Model I, Wald statistic with the variance being estimated under the null hypothesis, log- and logit-transformation statistics under Model II are recommended when sample size is small. In general, sample size formulae based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic and score statistic are recommended in practical applications. The applicability of the proposed methods is illustrated by a real-data example.

4 citations

Journal ArticleDOI
TL;DR: In this article, the optimal allocations of clusters and individuals are obtained under the assumption of equal cluster sizes, and the relative efficiency of unequal versus equal clusters sizes when estimating the average population proportion is obtained.
Abstract: Optimal sample sizes under a budget constraint for estimating a proportion in a two-stage sampling process have been derived using individual testing. However, when group testing is used, these optimal sample sizes are not appropriate. In this study, optimal sample sizes at the cluster and individual levels are derived for group testing. First, optimal allocations of clusters and individuals are obtained under the assumption of equal cluster sizes. Second, we obtain the relative efficiency (RE) of unequal versus equal cluster sizes when estimating the average population proportion, . By multiplying the sample of clusters obtained assuming equal cluster size by the inverse of the RE, we adjust the sample size required in the context of unequal cluster sizes. We also show the adjustments that need to be made to allocate clusters and individuals correctly in order to estimate the required budget and achieve a certain power or precision.

3 citations

01 Jan 2002
TL;DR: Inverse sampling and formal sequential designs may prove useful in reducing the sample size in studies where a small population proportion p is compared with a hypothesized reference proportion p 0 as mentioned in this paper, and the expected savings in sample size, when the alternative hypothesis is true, are 20% of the fixed sample size for the inverse sampling design and 40% for the triangular sequential design.
Abstract: Inverse sampling and formal sequential designs may prove useful in reducing the sample size in studies where a small population proportion p is compared with a hypothesized reference proportion p0. These methods are applied to the design of a cytogenetic study about chromosomal abnormalities in men with a daughter affected by Turner's syndrome. First it is shown how the calculated sample size for a classical design depends on the parameterization used. Later this sample size is compared with the required sample size in an inverse sampling design and a triangular sequential design using four different parameterizations (absolute differences, log-odds ratio, angular transform and Sprott's transform). The expected savings in sample size, when the alternative hypothesis is true, are 20% of the fixed sample size for the inverse sampling design and 40% for the triangular sequential design

3 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202112
202017
201914
201813
201713
201613