• Genomics & Informatics
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• v.4 no.2
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• pp.87-93
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• 2006

With the rapid development of MS technologiesy, the demands for a more sophisticated MS interpretation algorithm haves grown as well. We have developed a new protein fingerprinting method using a binomial distribution, (fBIND). With the fBIND, we improved the performance accuracy of protein fingerprinting up to the maximum 49% (more than MOWSE) and 2% than(at a previous binomial distribution approach studied by of Wool et al.) as compared to the established algorithms. Moreover, we also suggest a the statistical approach to define the significance of transcription factors and motifs in the identified proteins based on the Gene Ontology (GO). Abbreviations: fBIND, fingerprinting using binomial distribution; GO, Gene Ontology; MS, Mass Spectrometry; PMF, peptide mass fingerprinting; nr, nonredundant; SGD, Saccharomyces Genome Database

• The Pure and Applied Mathematics
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• v.4 no.2
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• pp.105-110
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• 1997

The distribution of staff in a hierachial organization has been studied in a variety of forms and models. Results here show that the promotion process follows a binomial distribution with parameters n and $\alpha=e^{-pt}$ and the recruitment process follows a poisson distribution with parameter $\lambda$. Futhermore, the mean time to promotion in the grade was estimated.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.821-830
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• 1999

In This paper we calculate the probabilities of binomial and Poisson distributions when n or${\mu}$ is large. Based on this calculation we consider the normal approximation to the binomial and binomial approximation to Poisson. We implement this approximation via CGI and dynamic graphs. These implementation are made available through the internet.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.23-32
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• 2007

We consider penalized likelihood regression with data from the negative binomial distribution with unknown shape parameter. Smoothing parameter selection and asymptotically efficient low dimensional approximations are employed for negative binomial data along with shape parameter estimation through several different algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.98-105
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• 2012

This paper describes the Bayesian approach for reliability demonstration test based on the samples from a finite population. The Bayesian approach involves the technical method about how to combine the prior distribution and the likelihood function to produce the posterior distribution. In this paper, the hypergeometric distribution is adopted as a likelihood function for a finite population. The conjugacy of the beta-binomial distribution and the hypergeometric distribution is shown and is used to make a decision about whether to accept or reject the finite population judging from a viewpoint of faulty goods. A numerical example is also given.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.313-322
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• 2015

A mixture distribution of a discrete uniform or degenerated distribution and two binomial distribution is proposed and a method of obtaining the maximum likelihood estimates of the parameters is provided. For the proposed model simulation studies were conducted to see performance of the maximum likelihood estimates and a mixture of a degenerated distribution and two binomial distributions was applied to fit a lecture evaluation data to show a good result.

• Kyungpook Mathematical Journal
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• v.57 no.3
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• pp.493-502
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• 2017

We propose an accurate approximation method via discrete Krawtchouk orthogonal polynomials to the distribution of a sum of independent but non-identically distributed binomial random variables. This approximation is a weighted binomial distribution with no need for continuity correction unlike commonly used density approximation methods such as saddlepoint, Gram-Charlier A type(GC), and Gaussian approximation methods. The accuracy obtained from the proposed approximation is compared with saddlepoint approximations applied by Eisinga et al. [4], which are the most accurate method among higher order asymptotic approximation methods. The numerical results show that the proposed approximation in general provide more accurate estimates over the entire range for the target probability mass function including the right-tail probabilities. In addition, the method is mathematically tractable and computationally easy to program.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.33-36
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• 2008

We introduce some properties for fuzzy binomial distributions with fuzzy valued probability. First we define fuzzy type I error and type II error for fuzzy relative frequency and agreement index. And we show that an fuzzy power function and fuzzy binomial frequency function for binomial proportion test.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.123-135
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• 2007

The objective of this study is to analyze the relationship between traffic accidents occurring at trumpet interchange ramps according to accident type as well as the relevant factors that led to the traffic accidents, such as geometric design elements and traffic volumes. In the process of analysis of the distribution of traffic accidents, negative binomial distribution was selected as the most appropriate model. Negative binomial regression models were developed for total trumpet interchange ramps, direct ramps, loop ramps and semi-direct ramps based on the negative binomial distribution. Based upon several statistical diagnostics of the difference between observed accidents and predicted accidents with four previously developed models, the fit proved to be reasonable. Understanding of statistically significant variables in the developed model will enable designers to increase efficiency in terms of road operations and the development of traffic accident prevention policies in accordance with road design features.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.397-410
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• 2007

For the two groups data from multivariate binomial distribution, we consider a bootstrap approach to inferring the simultaneous confidence level and its standard error of a collection of the dependent confidence intervals for the difference of proportions with an experimentwise error rate at the a level are presented. The bootstrap method is used to estimate the simultaneous confidence probability for the difference of proportions.