• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.585-594
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• 2003

It has been generally recognized that conventional binomial or Poisson model provides poor fits to the actual correlated binary data due to the extra-binomial variation. A number of generalized statistical models have been proposed to account for this additional variation. Among them, beta-binomial, correlated-binomial, and modified-binomial models are binomial-related models which are frequently used in modeling the sum of n correlated binary data. In many situations, it is reasonable to assume that n correlated binary data are exchangeable, which is a special case of correlated binary data. The sum of n exchangeable correlated binary data is modeled relatively well when the above three binomial-related models are applied. But the estimation results of correlation coefficient turn to be quite different. Hence, it is important to identify which model provides better estimates of model parameters(success probability, correlation coefficient). For this purpose, a small-scale simulation study is performed to compare the behavior of above three models.

• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.613-635
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• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.

• Asian-Australasian Journal of Animal Sciences
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• 제15권7호
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• pp.925-931
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• 2002

Gibbs sampling algorithms were implemented to the multi-trait threshold animal models with any combinations of multiple binary, ordered categorical, and linear traits and investigate the amount of bias on these models with two kinds of parameterization and algorithms for generating underlying liabilities. Statistical models which included additive genetic and residual effects as random and contemporary group effects as fixed were considered on the models using simulated data. The fully conditional posterior means of heritabilities and genetic (residual) correlations were calculated from 1,000 samples retained every 10th samples after 15,000 samples discarded as "burn-in" period. Under the models considered, several combinations of three traits with binary, multiple ordered categories, and continuous were analyzed. Five replicates were carried out. Estimates for heritabilities and genetic (residual) correlations as the posterior means were unbiased when underlying liabilities for a categorical trait were generated given by underlying liabilities of the other traits and threshold estimates were rescaled. Otherwise, when parameterizing threshold of zero and residual variance of one for binary traits, heritability estimates were inflated 7-10% upward. Genetic correlation estimates were biased upward if positively correlated and downward if negatively correlated when underling liabilities were generated without accounting for correlated traits on prior information. Residual correlation estimates were, consequently, much biased downward if positively correlated and upward if negatively correlated in that case. The more categorical trait had categories, the better mixing rate was shown.

• 대한화학회지
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• 제57권3호
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• pp.370-376
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• 2013

A back propagation artificial neural network model with one hidden layer is established to correlate the liquid-liquid equilibrium data of hydrocarbon-water systems. The model has four inputs and two outputs. The network is systematically trained with 48 data points in the range of 283.15 to 405.37K. Statistical analyses show that the optimised neural network model can yield excellent agreement with experimental data(the average absolute deviations equal to 0.037% and 0.0012% for the correlated mole fractions of hydrocarbon in two coexisting liquid phases respectively). The comparison in terms of average absolute deviation between the correlated mole fractions for each binary system and literature results indicates that the artificial neural network model gives far better results. This study also shows that artificial neural network model could be developed for the phase equilibria for a family of hydrocarbon-water binaries.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.349-361
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• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

• Korean Chemical Engineering Research
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• 제43권2호
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• pp.259-265
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• 2005

Water+n-methylformamide(NMF), benzene+NMF 그리고 toluene+NMF의 353.15 K 이성분계 등온 기-액상평형을 headspace gas chromatography(HSGC)로 측정하였고, NMF+benzene+n-heptane과 NMF+toluene+n-heptane 삼성분계에 대한 298.15 K 액-액상평형을 tie-line 측정법으로 결정하였다. 이성분계 기-액상평형 데이터는 공비점이 없었으며, $g^E$ 모델식(Margules, van Laar, Wilson, NRTL, UNIQUAC)에 비교적 작은 편차로 잘 상관되었다. 삼성분계 tie-line 데이터는 NRTL식과 UNIQUAC식을 이용하여 상관과 추산을 병행하였으며, Hirata-Fujita식과 Maior-Swenson식을 이용하여 정확도를 검증하였다.

• Korean Chemical Engineering Research
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• 제56권5호
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• pp.732-737
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• 2018

Experimental data of phase equilibria are reported for the binary mixture of 1-phenyl-2-pyrrolidone in supercritical carbon dioxide. Phase behavior data was measured in a synthetic method at a temperature ranging from 333.2 to 393.2 K and at pressures up to 97.14 MPa. The solubility of 1-phenyl-2-pyrrolidone in the carbon dioxide + 1-phenyl-2-pyrrolidone system increased as temperature increased at a constant pressure and it exhibited the type-I phase behavior. The experimental data for the binary mixture were correlated with the Peng-Robinson equation of state using mixing rule and the critical properties of 1-phenyl-2-pyrrolidone were predicted with the Joback and Lyderson method.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 대한화학회지
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• 제54권1호
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• pp.71-76
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• 2010

298.15~318.15 K 온도에서 디지탈 진동 U-tube densimeter 와 Ubbelohde 모세관 점성계을 사용하여 2-브로모프로판/메탄올 이성분 혼합물의 밀도와 점성도를 측정하였다. 온도와 농도에 대한 밀도와 점성도 상호 의존 관계를 조사하였다. 이성분 혼합물의 여분 몰부피와 여분 점성도를 실험으로 얻어진 밀도와 점성돌로부터 계산하여 구하였다. 모델이 실험치와 잘부합됨을 발견하였다.

• Korean Chemical Engineering Research
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• 제55권5호
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• pp.668-678
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• 2017

Intermolecular interactions were studied for binary mixtures of 2-propanol + cyclohexane, n-hexane, benzene, toluene, o-, m- and p-xylenes by measuring ultrasonic speeds (u) over the entire range of composition at 298.15 K and 308.15 K. From these results the deviation in ultrasonic speed was calculated. These results were fitted to the Redlich-Kister equation to derive the binary coefficients along with standard deviations between the experimental and calculated data. Acoustic parameters such as excess isentropic compressibility ($K_s^E$), intermolecular free length ($L_f$) and available volume ($V_a$) were also derived from ultrasonic speed data and Jacobson's free length theory. The ultrasonic speed data were correlated by Nomoto's relation, Van Dael's mixing relation, impedance dependence relation, and Schaaff's collision factor theory. Van Dael's relation gives the best prediction of u in the binary mixtures containing aliphatic hydrocarbons. The ultrasonic speed data and isentropic compressibility were further analyzed in terms of Jacobson's free length theory.