• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1543-1550
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• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.783-791
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• 2008

Estimation procedure of the finite population proportion and distribution function is considered. Based on a logistic regression model, an approximately model- optimal estimator is defined and conditions for the estimator to be design-consistent are given. Simulation study shows that the model-optimal design-consistent estimator defined under a logistic regression model performs well in estimating the finite population distribution function.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.587-593
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• 2012

We study Bayesian estimates for finite population proportions in multinomial problems. To do this, we consider a three-stage hierarchical Bayesian model. For prior, we use Dirichlet density to model each cell probability in each cluster. Our method does not require complicated computation such as Metropolis-Hastings algorithm to draw samples from each density of parameters. We draw samples using Gibbs sampler with grid method. We apply this algorithm to a couple of simulation data under three scenarios and we estimate the finite population proportions using two kinds of approaches We compare results with the point estimates of finite population proportions and their standard deviations. Finally, we check the consistency of computation using differen samples drawn from distinct iterates.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.501-509
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• 2009

Recently the interval estimation of binomial proportions is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the well-known Wald confidence interval. Various alternatives have been proposed. Among them, the Agresti-Coull confidence interval, the Wilson confidence interval and the Bayes confidence interval resulting from the noninformative Jefferys prior were recommended by Brown et al. (2001). However, unlike the binomial distribution case, little is known about the properties of the confidence intervals in finite population sampling. In this note, the property of confidence intervals is investigated in anile population sampling.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.1-12
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• 1995

Let F be a life distribution with finite mean $\mu$ Then F is said to be in new better then worse than used in expectation (NBWUE(p)) class if $\varphi(u) {\geq} u$ for $0 {\leq}u{\leq}t_0$ and ${\varphi}(u) {\leq} u$ for $t_0< u {\leq} 1$ where ${\varphi}(u)$ is the scaled total-time-on-test transform and $p=F(t_0)$. We propose a testing procedure for $H_0$ : F is exponential against $H_1$ : NBWUE(p), and is not expontial, (or $H_1\;'$ : F is NWBUE (p), and is not exponential) using randomly censored data. Our procedure assumes kmowledge of the proportion p of the population that fail at or before the change-point $\t_0$. Know ledge of $\t_0$ itself is not assumed. The asymptotic normality of the test statistic is established and a Monte Carlo experiment is performed to investigate the speed of convergence of the test statistic to normality. The power of our test is also studied.