• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.35-47
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• 2008

The low flow analysis is an important part in water resources engineering. Also, the results of low flow frequency analysis can be used for design of reservoir storage, water supply planning and design, waste-load allocation, and maintenance of quantity and quality of water for irrigation and wild life conservation. Especially, for identification of the uncertainty in frequency analysis, the Bayesian approach is applied and compared with conventional methodologies in at-site low flow frequency analysis. In the first manuscript, the theoretical background for the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) method and Metropolis-Hasting algorithm are studied. Two types of the prior distribution, a non-data- based and a data-based prior distributions are developed and compared to perform the Bayesian MCMC method. It can be suggested that the results of a data-based prior distribution is more effective than those of a non-data-based prior distribution. The acceptance rate of the algorithm is computed to assess the effectiveness of the developed algorithm. In the second manuscript, the Bayesian MCMC method using a data-based prior distribution and MLE(Maximum Likelihood Estimation) using a quadratic approximation are performed for the at-site low flow frequency analysis.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1089-1099
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• 2013

The Bradley-Terry model is widely used for analysis of pairwise preference data. We explain that the popularity of Bradley-Terry model is gained due to not only easy computation but also some nice asymptotic properties when the model is misspecified. For information retrieval required to analyze big ranking data, we propose to use a pseudo likelihood based on the Bradley-Terry model even when the true model is different from the Bradley-Terry model. We justify using the Bradley-Terry model by proving that the estimated ranking based on the proposed pseudo likelihood is consistent when the true model belongs to the class of Thurstone models, which is much bigger than the Bradley-Terry model.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.883-890
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• 2020

In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.

• Journal of Families and Better Life
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• v.10 no.1 s.19
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.177-187
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• 1997

This article considers the power performance of the tests in unbalanced two way mixed linear models with one fixed factor. The generalized least squares (GLS) F statistic testing no differences among the effects of the levels of the fixed factor is estimated using Henderson's method III, minimum norm quadratic unbiased estimator (MINQUE) with prior guess 1, maximum likelihood (ML) and resticted maximum likelihood (REML). We investigate the power performance of these test statistics. It can be shown, through simulation, that the GLS F statistics using four estimators produce similar type I error rates and power performance.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.167-177
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• 2006

An important role of statistical analysis in science is interpreting observed data as evidence, that is 'what do the data say?'. Although standard statistical methods (hypothesis testing, estimation, confidence intervals) are routinely used for this purpose, the theory behind those methods contains no defined concept of evidence and no answer to the basic question 'when is it correct to say that a given body of data represent evidence supporting one statistical hypothesis against another?' (Royall, 1997). In this article, we use likelihood ratios to measure evidence provided by record values in favor of a hypothesis and against an alternative. This hypothesis is concerned on mean of an exponential model and prediction of future record values.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.1003-1011
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• 2008

Mixed-effect Poisson regression models are widely used for analysis of correlated count data such as those found in longitudinal studies. In this paper, we consider kernel extensions with semiparametric 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.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.71-84
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• 2005

The Fieller-Creasy problem involves statistical inference about the ratio of two independent normal means. It is difficult problem from either a frequentist or a likelihood perspective. As an alternatives, a Bayesian analysis with noninformative priors may provide a solution to this problem. In this paper, we extend the results of Yin and Ghosh (2001) to unbalanced sample case. We find various noninformative priors such as first and second order matching priors, reference and Jeffreys' priors. The posterior propriety under the proposed noninformative priors will be given. Using real data, we provide illustrative examples. Through simulation study, we compute the frequentist coverage probabilities for probability matching and reference priors. Some simulation results will be given.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.257-262
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• 1996

When we are estimate the smoothing parameter in spline regression model, we deal the diagnostic of influence observations as posteriori analysis. When we use Generalized Maximum Likelihood Function as the estimation method of smoothing parameter, we propose the diagnostic measure for influencial observations in the obtained estimate, and we introduce the finding method of the proper smoothing parameter estimate.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.903-914
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• 2014

The inverse Weibull distribution has been proposed as a model in the analysis of life testing data. Also, inverse Weibull distribution has been recently derived as a suitable model to describe degradation phenomena of mechanical components such as the dynamic components (pistons, crankshaft, etc.) of diesel engines. In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the shape parameter in the inverse Weibull distribution under multiply type-II censoring. We also develop four modified empirical distribution function (EDF) type tests for the inverse Weibull or extreme value distribution based on multiply type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.