Estimation of population proportion in randomized response sampling using weighted confidence interval construction

Metrika ◽  
2011 ◽  
Vol 75 (5) ◽  
pp. 655-672 ◽  
Author(s):  
Horng-Jinh Chang ◽  
Mei-Pei Kuo
Keyword(s):  
Confidence Interval ◽  
Randomized Response ◽  
Population Proportion

scholarly journals Coverage versus Confidence

2021 ◽  
Vol 23 ◽  
Author(s):  
Peyton Cook
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Coverage Probability ◽  
Negative Binomial ◽  
Asymptotic Confidence Interval ◽  
Population Proportion ◽  
Coverage Probabilities ◽  
The Mean

This article is intended to help students understand the concept of a coverage probability involving confidence intervals. Mathematica is used as a language for describing an algorithm to compute the coverage probability for a simple confidence interval based on the binomial distribution. Then, higher-level functions are used to compute probabilities of expressions in order to obtain coverage probabilities. Several examples are presented: two confidence intervals for a population proportion based on the binomial distribution, an asymptotic confidence interval for the mean of the Poisson distribution, and an asymptotic confidence interval for a population proportion based on the negative binomial distribution.

scholarly journals Bayesian Estimation ofWarner’s Randomized Response Technique with Transmuted Kumaraswamy Prior

2021 ◽  
Author(s):  
Akinola Oladiran Adepetun ◽  
◽  
Bamidele Mustapha Oseni ◽  
Olusola Samuel Makinde ◽  
◽  
...  
Keyword(s):  
Tax Evasion ◽  
Unknown Parameter ◽  
Prior Information ◽  
Bayes Estimator ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Bayes Estimators ◽  
Population Proportion ◽  
Benchmark Data ◽  
Sensitive Attribute

In recent time, the Bayesian approach to randomized response technique has been used for estimating the population proportion especially of respondents possessing sensitive attributes such as induced abortion, tax evasion and shoplifting. This is done by combining suitable prior information about an unknown parameter of the population with the sample information for the estimation of the unknown parameter. In this study, possibility of using a transmuted Kumaraswamy prior is raised, yielding a new Bayes estimator for estimating population proportion of sensitive attribute for Warner’s randomized response technique. Consequently, the proposed Bayes estimator with transmuted Kumaraswamy prior is compared with existing Bayes estimators developed with a simple beta and Kumaraswamy priors in terms of their mean square error. The proposed estimator competes well with the existing estimators for some values of population proportion. The performances of Bayes estimators were also compared using some benchmark data.

scholarly journals Bayesian Analysis of Warner’s Randomized Response Technique

2017 ◽  
Vol 9 (1) ◽  
pp. 13-26
Author(s):  
A. O. Adepetun ◽  
A. A. Adewara
Keyword(s):  
Bayesian Analysis ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Sample Sizes ◽  
Large Sample ◽  
Population Proportion ◽  
Life Data ◽  
Sensitive Attribute ◽  
Alternative Beta ◽  
Survey Questionnaires

This paper proposed alternative beta estimators of the population proportion of a sensitive attribute when life data were obtained through the administration of survey questionnaires on abortion of some matured women. The results showed that the proposed alternative beta estimators were more efficient in capturing responses from respondents than the simple beta estimator proposed by Winkler and Franklin for relatively small, medium as well as large sample sizes respectively.

Optimal Randomized Response Models and Methods for Hypothesis Testing

1982 ◽  
Vol 7 (4) ◽  
pp. 295-310 ◽  
Author(s):  
John W. Carr ◽  
Leonard A. Marascuilo ◽  
Patricia Busk
Keyword(s):  
Confidence Interval ◽  
Hypothesis Testing ◽  
Self Report ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Response Models ◽  
Report Data ◽  
Asking Questions ◽  
Randomized Response Models ◽  
Self Report Data

This paper reviews several randomized response models for gathering self-report data when persons are asked sensitive questions, and introduces a new general model for reducing the standard error by asking questions conditional upon earlier answers. Hypothesis testing and confidence-interval procedures are demonstrated for data collected by the randomized response technique.

scholarly journals Prevalence of antibodies to Toxoplasma gondii in horses in the province of Kars, Turkey

2012 ◽  
Vol 49 (No. 1) ◽  
pp. 9-13 ◽  
Author(s):  
A. Akca ◽  
C. Babur ◽  
Arslan MO ◽  
Y. Gicik ◽  
M. Kara ◽  
...  
Keyword(s):  
Toxoplasma Gondii ◽  
Confidence Interval ◽  
Serum Samples ◽  
Population Proportion ◽  
Eastern Turkey ◽  
North Eastern

This study investigates the seroprevalence of Toxoplasma gondii in horses from seven villages in the province of Kars in north-eastern Turkey. A total of 189 serum samples from clinically healthy, local crossbred horses were tested for anti-T. gondii antibodies using the Sabin-Feldman dye test. Antibodies to T. gondii were found in 39 (20.6%) horses, with specific titres of 1 : 16 (27), 1 : 64 (11) and 1 : 256 (1). The 95% confidence interval for the population proportion ranged from 13.3 to 27.9%. The number of seropositive horses in Yucelen village (40%) was considerably higher than in other villages, whereas in the villages of Caglayan (8%) and Cerme (10.5%) the seroprevalence was lower than elsewhere. However, the differences in seroprevalence between the seven villages were not statistically significant (P > 0.05). In contrast, the overall seroprevalence in the province of Kars was significantly higher than that reported previously in studies carried out on army and private horse stud farms elsewhere in Turkey (P < 0.05). The relevance of these findings to the epizootiology of toxoplasmosis in Kars and Turkey is discussed.

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