Investigating the Impact of Simple and Mixture Priors on Estimating Sensitive Proportion Through a General Class of Randomized Response Models

Abid, Muhammad; Naeem, Ayesha; Hussain, Zawar; Riaz, Muhammad; Tahir, Muḥammad

doi:10.24200/sci.2018.20166

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…For the mixture of transmuted Weibull distribution, we assume different prior distributions such as gamma, inverse gamma, uniform, and beta for . These priors are selected by keeping in mind the range of the parameters [29]. Assuming independence, we have the joint prior distribution of the parameters…”

Section: The Posterior Distribution Using Ipmentioning

confidence: 99%

On the Bayesian Analysis of Two-Component Mixture of Transmuted Weibull Distribution

2019

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Transmuted distributions are skewed distributions and recently attracted a great attention of researchers due to their applications in reliability and statistics. In this article, our main focus is on the Bayesian estimation of two-component mixture of the Transmuted Weibull Distribution (TWD) under type-I right censored sampling scheme. In order to estimate the unknown parameters, non-informative and informative priors under Squared Error Loss Function (SELF), Precautionary Loss Function (PLF) and Quadratic Loss Function (QLF) are assumed when computing the posterior estimations. In addition, the Bayesian credible intervals (BCI) are also constructed. A Markov Chain Monte Carlo (MCMC) technique is adopted to generate samples from the posterior distributions and in turn computing different posterior summaries, including Bayes estimates (BEs), posterior risks (PRs) and Bayesian credible intervals (BCI). As an illustration, comparison of these Bayes estimators is made through simulated under different loss functions in terms of their respective posterior risks assuming different sample sizes and censoring rates. Two real-life examples, the first being the survival times of hepatitis B & C patients while the second being the hole diameter of 12 mm and the sheet thickness is 3.15 mm, are also discussed to illustrate the potential application of the proposed methodology.

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Section: The Posterior Distribution Using Ipmentioning

confidence: 99%

On the Bayesian Analysis of Two-Component Mixture of Transmuted Weibull Distribution

2019

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“…Some other useful references are, Irfan et al [17], Abid et al. [18], Abid et al [19], Javed et al [20], Naz et al [21], Younis and Shabbir [22], Ahmed and Shabbir [23] and Nazir et al [24].…”

Section: Introductionmentioning

confidence: 99%

On use of subsampling of the non-respondents for estimation of distribution function

Shabbir

Gupta

2021

Scientia Iranica

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In this study, we propose a general class of estimators of the finite population distribution function (DF) using two auxiliary variables under subsampling of non-respondents.We use the Hansen and Hurwitz pioneered model in our subsampling technique. Layout of response and non-response classes are discussed in various tables in detail. Expressions for the biases and mean square errors (MSEs) of the estimators are obtained up to first order of approximation. We also obtain the conditions by comparing the proposed estimator with existing estimators. Three real data sets are used to support the theoretical findings. In our findings, it is observed that the proposed class of estimators is more efficient as compared to all other existing estimators including the usual mean estimator, ratio estimator, exponential-ratio estimator, traditional difference estimator, and many well-known difference type estimators by using the criterion of MSE.

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“…Greenberg et al [3] extended the Warner's model by introducing unrelated innocuous attribute say, X as a replacement of A c in their RRT model. Some other developments in RRT are due to Chaudhuri and Mukerjee [4], Mahmood et al [5], Perri [6], Hussain and Shabbir [7], Lee et al [8], Abdelfatah and Mazloum [9], Tanveer and Singh [10; 11], Blair et al [12], Singh and Gorey [13], Bose [14] and Abid et al [15].…”

Section: Introductionmentioning

confidence: 99%

Estimating Prevalence of Sensitive Attribute with Optional Unrelated Question Randomized Response Models under Simple and Stratified Random Sampling

Narjis

Shabbir

2019

Scientia Iranica

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In this study, we propose optional randomized response technique (RRT) models in binary response situation. The utility of proposed optional RRT models under stratification are also explored. Gupta et al.[1] introduced an ingenious idea of optional RRT model, where question may be sensitive for one respondent but may not be sensitive for another. This study focus on estimating π, the prevalence of sensitive attribute, ω, the sensitivity level of the underlying sensitive question when the proportion of unrelated innocuous attribute π x is unknown. A new multi-question approach is proposed for estimation of parameters (π, ω). A comparison between proposed optional RRT models and corresponding full RRT models are carried out numerically under simple and stratified random sampling.

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Investigating the Impact of Simple and Mixture Priors on Estimating Sensitive Proportion Through a General Class of Randomized Response Models

Cited by 3 publications

References 29 publications

On the Bayesian Analysis of Two-Component Mixture of Transmuted Weibull Distribution

On the Bayesian Analysis of Two-Component Mixture of Transmuted Weibull Distribution

On use of subsampling of the non-respondents for estimation of distribution function

Estimating Prevalence of Sensitive Attribute with Optional Unrelated Question Randomized Response Models under Simple and Stratified Random Sampling

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