• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.161-168
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• 1996

This paper describes a sampling method, which can be used instead of the simple random sampling without replacement(SRSWOR). This method, circular sampling, assumes that the sampling units of the population are arranged in circular format, and randomly selects as many as samples of contiguous units. Therefore this method gathers information quicker and easier than STSWOR. In certain circumstances, the reliability of this method is better than that of STSWOR. And of circular sampling would be applied to nonprobability could be determined. methods, the reliability of the sample results in terms of probability could be determined.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.8
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• pp.60-66
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• 2013

The wind-speeds among geographically close wind-farms have high correlations seasonally. This paper presents a novel wind-speed sampling method which sincerely reflects the correlation among wind-speeds of different wind-farms. In the proposed method, the wind-speed samples are generated through the statistical data analysis of the measured past wind-speed data and are adequate to be applied to generation adequacy assessment based on random sampling. In the proposed method, the specific probability distribution need not to be assumed and sufficiently accurate wind-speed samples can be generated based only on the measured past data. The proposed method is applied to the two wind-farm problem to show its applicability.

• Journal of the Korean Institute of Telematics and Electronics
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• v.18 no.2
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• pp.22-26
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• 1981

Truncated expansions based upon the sampling theorem but containing only a few terms can be very useful for practical interpolations of band-limited or narrow-band random signals. The major goal of this work is to find and coiupare efficient and "statistically accurate" algorithms for the dynamic analysis of the ocean systems. The stalistical accuracy of truncated sampling interpolations is investicated, and one simple ocean systems, which yields a Runge-Kutta simulation algorithm of improved accuracy with very little increase in computation, is indicated.indicated.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.105-115
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• 2008

Judgement post-stratification is a new sampling method developed by MacEachern et al. (2004). This article suggests that the judgement post-stratification method can be a good alternative for the simple random sampling when analyzing real-world environmental data. It becomes an important task to accurately measure the output of a recycling facility since the EPR (Extended Producer Responsibility) system takes effect on 2003. However, the total weight of materials processed in the recycling facility may not be a proper measure because the materials are frequently mingled with other non-recycling materials. Therefore, it is necessary to estimate the mixture ratio of non-recycling materials among the total materials admitted in the facility. Unfortunately, the size of sample in a recycling facility is restricted due to the inconvenience of sampling procedure such as safety, odor, time and classification of non-recycling materials. In this article, we showed the relative efficiency of the judgement post-stratification method over the simple random sampling method for equal sample sizes using Monte Carlo simulation. Furthermore, we applied the judgement post-stratification method on the 2004 recycling data and showed that it can replace the simple random sampling even with smaller observations.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.45-60
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• 2014

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.10
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• pp.513-519
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• 2001

This paper proposed a random sigma-delta modulator(RSDM), which is constructed by a 1st order sigma-delta modulator(SDM) and a simple structured random binary generator(RBG). The 1st order SDM produces a switching pulse waveform which has the same low-frequency component as the reference input, while the RBG spreads the distribution of the number of sampling per switching cycle, and thus disperses the spectrum spikes in the output. The relationship between the harmonic spectra and the number of sampling per switching cycle is studied through computer simulations, and the frequency spectra of the RSDM are confirmed in an experimental setup.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.107-116
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• 1999

We propose a sample design which minimize Bayes risk for cluster smpling in sampling inspection. We treat a pilot sample and an additional sample size as random variable. In addition we compute an appropriate cluster size for handling over-dispersion.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.379-386
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• 2004

This paper deals with a mixed logit model for vaccination data. The effect of a newly developed vaccine for a certain chicken disease can be evaluated by a noninfection rate after injecting chicken with the disease vaccine. But there are a lot of factors that might affect the noninfecton rate. Some of these are fixed and others are random. Random factors are sometimes coming from the sampling scheme for choosing experimental units. This paper suggests a mixed model when some fixed factors need to have different experimental sizes by an experimental design and illustrates how to estimate parameters in a suggested model.

• International Journal of Contents
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• v.2 no.2
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• pp.17-24
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• 2006

The work presented in this paper is divided into two parts. The first part presents finite urn problems which generate truncated negative binomial random variables. Some combinatorial identities that arose from the negative binomial sampling and truncated negative binomial sampling are established. These identities are constructed and serve important roles when we deal with these distributions and their characteristics. Other important results including cumulants and moments of the distributions are given in somewhat simple forms. Second, the distributions of the maximum of two chi-square variables and the distributions of the maximum correlated F-variables are then derived within the negative binomial sampling scheme. Although multinomial theory applied to order statistics and standard transformation techniques can be used to derive these distributions, the negative binomial sampling approach provides more information and deeper insight regarding the nature of the relationship between the sampling vehicle and the probability distributions of these functions of chi-square variables. We also provide an algorithm to compute the percentage points of these distributions. We supplement our findings with exact simple computational methods where no interpolations are involved.