• Speech Sciences
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• v.7 no.4
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• pp.27-40
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• 2000

The goal of quantitative linguistics is to show the quantitative behavior of linguistic units. There are several studies which examine the frequency of Korean phonemes, which are important in comprehending the internal function of the linguistic units. However, the frequency information, from the pure phonological level without any consideration of rhythmic group, cannot adequately represent linguistic phenomena. Therefore, to provide the effective information, the phonological transcription must be carried out on the level of rhythmic group. In this paper, we made the transcription to analyze Korean phonology. We were not satisfied with merely investigating the frequencies of the phonemes, but also examined whether the distribution of Korean phonemes show the binomial distribution within linguistic constraints.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.85-92
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• 2016

The law of large numbers, central limit theorem, and connection among binomial distribution, normal distribution, and statistical estimation require dynamics of continuous visualization for students' better understanding of the concepts. During this visualization process, the differences and similarities between statistical probability and mathematical probability that students should observe need to be provided with the intermediate steps in the converging process. We propose a visualization method that can integrate intermediate processes and results through Excel. In this process, students' experiences with dynamic visualization help them to perceive that the results are continuously changed and extracted from multiple situations. Considering modeling as a key process, we developed a classroom exercise using Excel to estimate the population mean and standard deviation by using a sample mean computed from a collection of data out of the population through sampling.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.733-749
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• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.852-861
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• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.153-161
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• 2017

Branching processes which is used for epidemic dispersion as stochastic process model have advantages to estimate parameters by real data. We have to estimate both mean and dispersion parameter in order to use the negative binomial distribution as an offspring distribution on branching processes. In existing studies on biology and epidemiology, it is estimated using maximum-likelihood methods. However, for most of epidemic data, it is hard to get the best precision of maximum-likelihood estimator. We suggest a Bayesian inference that have good properties of statistics for small-sample. After estimating dispersion parameter we modelled the posterior distribution for 2015 Korea MERS cases. As the result, we found that the estimated dispersion parameter is relatively stable no matter how we assume prior distribution. We also computed extinction probabilities on branching processes using estimated dispersion parameters.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.581-589
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• 2005

The purpose of this thesis is to develop and introduce Add-in program which we can systematically, visually and dynamically study discrete probability distribution of binomial distribution, poisson distribution and hypergeometric distribution, and continuous probability distribution of normal distribution, exponential distribution, and the definition and characteristics of t distribution, F distribution and ${\chi}^2$ distribution to be driven from normal distribution, and graphs, the computation process of probability by using VBA which is the device of Excel.

• Journal of the Korean Statistical Society
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• v.15 no.1
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• pp.71-78
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• 1986

In this paper, multivariate discrete distribution is dealt with, where a set of r distinct counts are misreported as another set of r counts. First, the variance for the one variable marginal case is expressed in the form of an inverted parabola. Next, for the multivariate negative binomial case, elements of the covariance matrix are evaluated with reference to asymptotic distributions. Finally, for the same case of multivariate negative binomial, Bayesian estimates of the parameters and of the modification rates are provided.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.233-244
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• 2006

In this paper, we develop the Bayesian multiple comparisons procedure for the binomial distribution. We suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. An example is illustrated for the proposed method. For this example, the suggested method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison. Also, some simulation was performed.

• Journal of the Korean Statistical Society
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• v.6 no.1
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• pp.3-20
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• 1977

In many applications one is concerned with repeated Bernoulli trials whose parameter (success probability) is usually unknown and has to be estimated from a sample. The probability distribution and statistical inference on the repeated independent Bernoulli trials have been studied extensively for the cases of fixed sample size sampling plan, and inverse binomial sampling plan in which observations are cotinued until a pressigned number of successes are obtained. See, for example, Haldane, Girschick et al., DeGroot and Johnson and Kotz.

• Honam Mathematical Journal
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• v.5 no.1
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• pp.251-260
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• 1983