• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.465-472
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• 1999

We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

• STI Policy Review
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• v.2 no.2
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• pp.45-54
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• 2011

This paper develops a recursive multinomial probit model and describes its estimation method. The recursive multinomial probit model is an extension of a recursive bivariate probit model. The main difference between the two models is that a single decision among two or more alternatives can be considered in each choice equation in the proposed model. The recursive multinomial probit model is developed based on a standard framework of the multinomial probit model and a Bayesian approach with a Gibbs sampling is adopted for the estimation. The simulation exercise with artificial data sets is showed that the model performed well. Since the recursive multinomial probit model can be applied to analyze the causal relationship between discrete dependent variables with more than two outcomes, the model can play an important role in extending the methodology of the causal relationship analysis in innovation research.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.185-191
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• 1997

Bayesian estimation of multinomial and Poisson parameters under starshped restriction is considered. Most Bayesian estimations in order restricted statistical inference require the high-dimensional integration which is very difficult to evaluate. Monte Carlo integration and Gibbs sampling are among alternative methods. The Bayesian estimation considered in this paper requires only evaluation of incomplete beta functions which are extensively tabulated.

• Environmental and Resource Economics Review
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• v.11 no.4
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• pp.609-632
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• 2002

국민소득이 빠르게 증가함에 따라 1990년대 이후 가정용 난방연료의 소비구조 역시 크게 변화하고 있다. 본 연구는 에너지 및 교통수요분석에 많이 사용되는 Multinomial Probit 모형을 이용하여 가정용 난방연료의 선택 행태를 분석하였다. 모형의 추정방법으로는 베이지안(Baysian) 방법론에 의한 Gibbs Sampling기법 (McColluch et al., 2000)을 이용하여 Multinomial probit 모형에서 선택대안이 3개 이상일 경우 발생할 수 있는 추정상의 어려움을 극복하였다. 한국가구패널조사(KHPS) 자료를 이용하여 서울과 경기도 대도시 지역을 대상으로 분석한 결과, 석유와 천연가스가 연탄에 비해 더 밀접한 상호 대체관계를 가지고 있는 것으로 나타났다. 또한 소득이 높은 가구일수록 천연가스에 대한 선호도가 더 높은 것으로 나타나서 향후 공급망 확대에 따라 난방연료용 가스 소비가 더욱 늘어날 것으로 예상된다.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.435-451
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• 2001

We describe a hierarchical bayesian model to analyze multinomial nonignorable nonresponse data. Using a Dirichlet and beta prior to model the cell probabilities, We develop a complete hierarchical bayesian analysis for multinomial proportions without making any algebraic approximation. Inference is sampling based and Markove chain Monte Carlo methods are used to perform the computations. We apply our method to the dta on body mass index(BMI) and show the model works reasonably well.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.327-336
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• 1999

In a multinomial sampling scheme some categories may be observed as partially classified because of technical or economic reasons. In this paper the maximum likelihood estimators(M.L.E) of multinomial probabilities are obtained when some frequencies are merged. We obtained the M.L.E and a method to evaluate the information gained by including merged frequencies in M.L.E we obtained an estimator of the covariance matrix and it is used to examine the information gained by including the merged frequency counts in estimating the cell probabilities. When certain individual frequency counts are missing a method is proposed for estimating the cell probabilities using EM algorithms.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.67-77
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• 2010

In multinomial scheme with step-wise sampling, maximum likelihood estimates of multinomial probabilities are improved when some frequencies are merged. In this study, for cell probabilities in a I by J independent contingency tables, exact MLE and step-wise estimation methods are applied and the results are compared using MSE and Bias.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.1
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• pp.80-89
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• 2021

In Korea, marine fish landed at ports are randomly sampled on a periodic basis (e.g., daily or weekly), and body sizes (e.g., lengths and weights) of those sampled fish are measured. The motivation for our study is whether or not such measurements reflect the size distribution, especially the length distribution of fish landed (= a population), because such length measurements are key data for a length-based assessment model. The current sampling method is to sample fish landed at ports by body size group (e.g., very small, small, medium, large, very large), using the sampling weights as the number of boxes by body size group. In this study, we showed that length composition data about fish sampled by the current method did not represent the length frequency distribution of the fish landed, and suggested that an alternative sampling method should be applied of using the sampling weights as the number of fish landed by body size group. We also introduced a method for determining an appropriate sample size.

• 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.

• International Journal of Contents
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• v.3 no.1
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• pp.1-8
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• 2007

The distributions of the minimum correlated F-variable arises in many applied statistical problems including simultaneous analysis of variance (SANOVA), equality of variance, selection and ranking populations, and reliability analysis. In this paper, negative binomial sampling technique is employed to derive the distributions of the minimum of chi-square variables and hence the distributions of the minimum correlated F-variables. The work presented in this paper is divided in two parts. The first part is devoted to develop some combinatorial identities arised from the negative binomial sampling. These identities are constructed and justified to serve important purpose, when we deal with these distributions or their characteristics. Other important results including cumulants and moments of these distributions are also given in somewhat simple forms. Second, the distributions of minimum, chisquare variable and hence the distribution of the minimum correlated F-variables are then derived within the negative binomial sampling framework. 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 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 the distributions. The computation methods we adopted are exact and no interpolations are involved.