• Communications for Statistical Applications and Methods
• /
• v.28 no.6
• /
• pp.643-653
• /
• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• Journal of Korean Institute of Industrial Engineers
• /
• v.5 no.1
• /
• pp.7-17
• /
• 1979

A procedure for constructing a double sampling plan with zero acceptance number for the first sample whose combined sample size does nat exceed that of the given single or double sampling plan is studied. Using this procedure a double sampling system is proposed that can be used in place of MIL-STD-105D system when the quality history is good, and its composite quality characteristics are tabulated and compared with those of MIL-STD-105D system. The study reveals that composite ASN's of the proposed double sampling system are considerably smaller than those of corresponding MIL-STD-105D system with no appreciable changes in other composite quality characteristics. The procedure is also applied to K SA 3102 plans with similar results. A cost model that can be used in comparing a given single sampling plan with the proposed double sampling plan is also developed.

• Communications for Statistical Applications and Methods
• /
• v.19 no.6
• /
• pp.751-759
• /
• 2012

This paper proposes an alternative stratified randomized response model based on the model of Singh and Joarder (1997). It is shown numerically that the proposed stratified randomized response model is more efficient than Hong et al. (1994) (under proportional allocation) and Kim and Warde (2004) (under optimum allocation).

• The Korean Journal of Applied Statistics
• /
• v.24 no.4
• /
• pp.677-693
• /
• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• The Korean Journal of Applied Statistics
• /
• v.27 no.2
• /
• pp.239-247
• /
• 2014

For a sensitive survey in which the population is composed by several strata with quantitative attributes, we present an additive stratified quantitative attribute randomized response model which applied stratified random sampling instead of simple random sampling to the models of Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's. We also establish theoretical grounds to estimate the stratum mean of sensitive quantitative attributes as well as the over all mean. We deal with the proportional and optimal allocation problems in each suggested model and compare the relative efficiency of the suggested two models; subsequently, Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of stratified random sampling.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1155-1168
• /
• 2016

This paper considers a probability density estimation problem of climate values. In particular, we focus on estimating probability densities of summer extreme temperature over South Korea. It is known that the probability density of climate values at one location is similar to those at near by locations and one doesn't follow well known parametric distributions. To accommodate these properties, we use a mixture of conditional autoregressive species sampling model, which is a nonparametric Bayesian model with a spatial dependency. We apply the model to a dataset consisting of summer maximum temperature and minimum temperature over South Korea. The dataset is obtained from University of East Anglia.

• Korean Journal of Hydrosciences
• /
• v.4
• /
• pp.51-63
• /
• 1993

This study is to verify the applicability of statistical models in predicting flood frequency at the stage gaging stations of which the flow is under natural condition in the Han River basin. The results of the study show that the statistical flood frequency models were proven to be fairly reasonable to apply in practice, and also were compared with sampling variance to calibrate the statistical efficiency of the estimators of the T year floods Q(T) by two different flood frequency models. As a result, it was showed that for return periods greater than about T = 10 years the annual exceedance series estimators of Q(T) has smaller sampling variance than the annual maximum series estimators. It was showed that for the range of return periods the partial duration series estimators of !(T) has smaller sampling variance than the annual maximum series estimate only if the POT model contains at least 2N(N : record length) items or more in order to estimate Q(T) more efficiently than the ANNMAX model.

• Journal of Korean Society for Quality Management
• /
• v.7 no.2
• /
• pp.17-21
• /
• 1979

There are many kinds of the mathematical models which are developed for choosing the economic inspection plan. The aim of this paper is to classify these mathematical models, and to examine their characteristics. The mathematical models for choosing the economic inspection plan can be classified into three groups. The first of it is the break-even analysis, the second group of the model is to choose the inspection plan so as to minimize total sampling inspection cost function, and the third group of it is the model to choose the inspection plan which maximize the profit function of the sampling inspection. As a result of examining the characteristics of this classified group of the models the model to choose the inspection plan which minimize total sampling inspection cost is more economical than the other models.

• Journal of Microbiology and Biotechnology
• /
• v.16 no.5
• /
• pp.683-688
• /
• 2006

We have developed a program for generating shotgun data sets from known genome sequences. Generation of synthetic data sets by computer program is a useful alternative to real data to which students and researchers have limited access. Uniformly-distributed-sampling clones that were adopted by previous programs cannot account for the real situation where sampled reads tend to come from particular regions of the target genome. To reflect such situation, a probabilistic model for biased sampling distribution was developed by using an experimental data set derived from a microbial genome project. Among the experimental parameters tested (varied fragment or read lengths, chimerism, and sequencing error), the extent of sequencing error was the most critical factor that hampered sequence assembly. We propose that an optimum sequencing strategy employing different insert lengths and redundancy can be established by performing a variety of simulations.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.175-188
• /
• 2015

This study is about estimation methods for the population pharmacokinetic and pharmacodymic model. This is a nonlinear mixed effect model, and it is difficult to find estimates of parameters because of nonlinearity. In this study, we examined theoretical background of various estimation methods provided by NONMEM, which is the most widely used software in the pharmacometrics area. We focused on estimation methods using a stochastic sampling approach - IMP, IMPMAP, SAEM and BAYES. The SAEM method showed the best performance among methods, and IMPMAP and BAYES methods showed slightly less performance than SAEM. The major obstacle to a stochastic sampling approach is the running time to find solution. We propose new approach to find more precise initial values using an ITS method to shorten the running time.