• 제목/요약/키워드: binomial

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이항트리에서 S-에지번호 매김 (The S-Edge Numbering on Binomial trees)

  • 김용석
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2004년도 하계종합학술대회 논문집(1)
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    • pp.167-170
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    • 2004
  • We present a novel graph labeling problem called S-edge labeling. The constraint in this labeling is placed on the allowable edge label which is the difference between the labels of endvertices of an edge. Each edge label should be ${ a_n / a_n = 4 a_{n-l}+l,\;a_{n-1}=0}$. We show that every binomial tree is possible S-edge labeling by giving labeling schems to them. The labelings on the binomial trees are applied to their embedings into interconnection networks.

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ESTIMATING THE SIMULTANEOUS CONFIDENCE LEVELS FOR THE DIFFERENCE OF PROPORTIONS FROM MULTIVARIATE BINOMIAL DISTRIBUTIONS

  • Jeong, Hyeong-Chul;Jhun, Myoung-Shic;Lee, Jae-Won
    • Journal of the Korean Statistical Society
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    • 제36권3호
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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.

Bayesian Analysis for Random Effects Binomial Regression

  • Kim, Dal-Ho;Kim, Eun-Young
    • Communications for Statistical Applications and Methods
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    • 제7권3호
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    • pp.817-827
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    • 2000
  • In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

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Constrained Estimation of the Numbers of Trials in Several Binomial Populations

  • Oh, Myongsik;Lee, Eun-Kyoung
    • Communications for Statistical Applications and Methods
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    • 제7권3호
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    • pp.699-709
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    • 2000
  • The constrained maximum likelihood estimation of the number of trials in several binomial populations under order restriction, such as simple order, is discussed. The estimation procedure is based on, so called, pool adjacent violators algorithm. Three handy estimators are given and their performances are compared using an artificial example.

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Hierarchical Bayesian Inference of Binomial Data with Nonresponse

  • Han, Geunshik;Nandram, Balgobin
    • Journal of the Korean Statistical Society
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    • 제31권1호
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    • pp.45-61
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    • 2002
  • We consider the problem of estimating binomial proportions in the presence of nonignorable nonresponse using the Bayesian selection approach. Inference is sampling based and Markov chain Monte Carlo (MCMC) methods are used to perform the computations. We apply our method to study doctor visits data from the Korean National Family Income and Expenditure Survey (NFIES). The ignorable and nonignorable models are compared to Stasny's method (1991) by measuring the variability from the Metropolis-Hastings (MH) sampler. The results show that both models work very well.

THE BINOMIAL METHOD FOR A MATRIX SQUARE ROOT

  • Kim, Yeon-Ji;Seo, Jong-Hyeon;Kim, Hyun-Min
    • East Asian mathematical journal
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    • 제29권5호
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    • pp.511-519
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    • 2013
  • There are various methods for evaluating a matrix square root, which is a solvent of the quadratic matrix equation $X^2-A=0$. We consider new iterative methods for solving matrix square roots of M-matrices. Particulary we show that the relaxed binomial iteration is more efficient than Newton-Schulz iteration in some cases. And we construct a formula to find relaxation coefficients through statistical experiments.

Mixed Effects Kernel Binomial Regression

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제19권4호
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    • pp.1327-1334
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    • 2008
  • Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

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A simple zero inflated bivariate negative binomial regression model with different dispersion parameters

  • Kim, Dongseok
    • Journal of the Korean Data and Information Science Society
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    • 제24권4호
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    • pp.895-900
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    • 2013
  • In this research, we propose a simple bivariate zero inflated negative binomial regression model with different dispersion for bivariate count data with excess zeros. An application to the demand for health services shows that the proposed model is better than existing models in terms of log-likelihood and AIC.

BINOMIAL PROMOTION AND POISSON RECRUITMENT MODEL FOR MANPOWER DEVELOPMENT

  • Etuk, U.H.
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제4권2호
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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.

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A NOTE ON TWO KNOWN SUMS INVOLVING CENTRAL BINOMIAL COEFFICIENTS WITH AN APPLICATION

  • Lim, Dongkyu;Rathie, Arjun Kumar
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제29권2호
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    • pp.171-177
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    • 2022
  • The aim of this note is to establish two known sums involving central binomial coefficients via a hypergeometric series approach. As an application, we discover two new closed-form evaluations of generalized hypergeometric function.