• Journal of Korea Water Resources Association
• /
• v.40 no.11
• /
• pp.851-859
• /
• 2007

In this study, the chukwookee data were evaluated by applying that for the historical rainfall frequency analysis. To derive a two parameter log-normal distribution by using historical data and modem data, censored data MLE and binomial censored data MLE were applied. As a result, we found that both average and standard deviation were all estimated smaller with chukwookee data then those with only modern data. This indicates that rather big events rarely happens during the period of chukwookee data then during the modern period. The frequency analysis results using the parameters estimated were also similar to those expected. The point to be noticed is that the rainfall quantiles estimated by both methods were similar. This result indicates that the historical document records like the annals of Chosun dynasty could be valuable and effective for the frequency analysis. This also means the extension of data available for frequency analysis.

• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.583-592
• /
• 2015

Zero-inflation has recently attracted much attention in integer-valued time series. This article deals with conditional variance (volatility) modeling for the zero-inflated count time series. We incorporate zero-inflation property into integer-valued GARCH (INGARCH) via conditional Poisson and negative binomial marginals. The Cholera frequency time series is analyzed as a data application. Estimation is carried out using EM-algorithm as suggested by Zhu (2012).

• Communications for Statistical Applications and Methods
• /
• v.17 no.6
• /
• pp.811-827
• /
• 2010

This paper deals with a density estimation method in binary choice models that can be regarded as a statistical inverse problem. We use an orthogonal basis to estimate density function and consider the choice of an appropriate truncation parameter to reflect the model complexity and the prediction accuracy. We propose a data-dependent rule to choose the truncation parameter in the context of binary choice models. A numerical simulation is provided to illustrate the performance of the proposed method.

• International Journal of Highway Engineering
• /
• v.13 no.2
• /
• pp.49-56
• /
• 2011

Understanding various casual factors affecting the occurrence of freeway traffic crash is a backbone of deriving effective countermeasures. The first step toward understanding such factors is to identify crash risks on freeways. Unlike existing studies, this study focused on the unsafe vehicle maneuvering that can be detected by in-vehicle sensors. The recent advancement of sensor technologies allows us to gather and analyze detailed microscopic events leading to crash occurrence such as the abrupt change in acceleration. This study used an accelerometer to capture the unsafe events. A set of candidate variables representing unsafe events were derived from analyzing acceleration data obtained by the accelerometer. Then, the crash risk was modeled by the binary logistic regression technique. The probabilistic outcome of crash risk can be provided by the proposed model. An application of the methodology assessing crash risk was presented, and further research items for the successful field implementation were also discussed.

• The Korean Journal of Applied Statistics
• /
• v.20 no.1
• /
• pp.133-145
• /
• 2007

Local linear estimator has a number of advantages over the traditional kernel estimators. The better performance near boundaries is one of them. However, local linear estimator can produce erratic result in sparse regions in the realization of the design and to solve this problem much research has been done. Local linear quasi-likelihood estimator has many common properties with local linear estimator, and it turns out that sparse design can also lead local linear quasi-likelihood estimator to erratic behavior in practice. Several methods to solve this problem are proposed and their finite sample properties are compared by the simulation study.

• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.571-579
• /
• 2011

We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.

• Journal of Korea Port Economic Association
• /
• v.28 no.1
• /
• pp.53-82
• /
• 2012

The purpose of this paper is to show the clustering trend by using the context-dependent and measure-specific models for 38 Asian ports during 10 years(2001-2009) with 4 inputs and 1 output. The main empirical results of this paper are as follows. First, clustering results by using context-dependent and measure-specific models are same. Second, the most efficient clustering was shown among the Hong Kong, Singapore, Ningbo, Guangzhou, and Kaosiung ports. Third, Port Sultan Qaboos, Jeddah, and Aden ports showed the lowest level clustering. Fourth, ranking order of attractiveness is Guangzhou, Dubai, HongKong, Ningbo, and Shanghai, and the results of progressive scores confirmed that low level ports can increase their efficiency by benchmarking the upper level ports. Fifth, benchmark share showed that Dubai(birth length), and HongKong(port depth, total area, and no. of cranes) have affected the efficiency of the inefficient ports.

• The Korean Journal of Applied Statistics
• /
• v.33 no.1
• /
• pp.47-59
• /
• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• The Korean Journal of Applied Statistics
• /
• v.22 no.2
• /
• pp.365-373
• /
• 2009

Since 1990, identifying the sex of fetus and illegal abortion has brought the sex ratio imbalance at birth in Korea due to a notion of preferring a son to a daughter, socio-economic development, population policy, and so forth. Although there have been many researches such as time series analysis and region difference analysis to monitor this sex ratio imbalance, they have a defect that time and space could not be included in the analysis simultaneously. This study analyzes the sex ratio imbalance at birth, taking into account time and region at the same time. The analysis considered the numbers of male and female babies, who were born as the third or latter in their families, in 2000 and 2001 at 234 Gu / Si / Goon administrative districts. Here, we suggest a mixture model of binomial distributions, assuming heterogeneous populations. The estimation of the location parameters, weights and correlation coefficient of the mixture model is conducted by the EM algorithm, and the heterogeneity of the regions is expressed as a picture using ArcView GIS.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.231-239
• /
• 2015

A Poisson model is the first choice for counts data. Quasi Poisson or negative binomial models are usually used in cases of over (or under) dispersed data. However, these models might be unsuitable if the data consist of excessive number of zeros (zero inflated data). For zero inflated counts data, Zero Inflated Poisson (ZIP) or Zero Inflated Negative Binomial (ZINB) models are recommended to address the issue. In this paper, we further considered a situation where zero inflated data are spatially correlated. A mixed effect model with random effects that account for spatial autocorrelation is used to fit the data.