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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 21, Issue 6 - Nov 2014
Volume 21, Issue 5 - Sep 2014
Volume 21, Issue 4 - Jul 2014
Volume 21, Issue 3 - May 2014
Volume 21, Issue 2 - Mar 2014
Volume 21, Issue 1 - Jan 2014
Selecting the target year
Forecasting Symbolic Candle Chart-Valued Time Series
Park, Heewon ; Sakaori, Fumitake ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 471~486
DOI : 10.5351/CSAM.2014.21.6.471
This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.
Estimating the Transmittable Prevalence of Infectious Diseases Using a Back-Calculation Approach
Lee, Youngsaeng ; Jang, Hyun Gap ; Kim, Tae Yoon ; Park, Jeong-Soo ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 487~500
DOI : 10.5351/CSAM.2014.21.6.487
A new method to calculate the transmittable prevalence of an epidemic disease is proposed based on a back-calculation formula. We calculated the probabilities of reactivation and of parasitemia as well as transmittable prevalence (the number of persons with parasitemia in the incubation period) of malaria in South Korea using incidence of 12 years(2001-2012). For this computation, a new probability function of transmittable condition is obtained. The probability of reactivation is estimated by the least squares method for the back-calculated longterm incubation period. The probability of parasitemia is calculated by a convolution of the survival function of the short-term incubation function and the probability of reactivation. Transmittable prevalence is computed by a convolution of the infected numbers and the probabilities of transmission. Confidence intervals are calculated using the parametric bootstrap method. The method proposed is applicable to other epidemic diseases in other countries where incidence and a long incubation period are available. We found the estimated transmittable prevalence in South Korea was concentrated in the summer with 276 cases on a peak at the
week and with about a 60% reduction in the peak from the naive prevalence. The statistics of transmittable prevalence can be used for malaria prevention programs and to select blood transfusion donors.
Regime-dependent Characteristics of KOSPI Return
Kim, Woohwan ; Bang, Seungbeom ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 501~512
DOI : 10.5351/CSAM.2014.21.6.501
Stylized facts on asset return are fat-tail, asymmetry, volatility clustering and structure changes. This paper simultaneously captures these characteristics by introducing a multi-regime models: Finite mixture distribution and regime switching GARCH model. Analyzing the daily KOSPI return from
January 2000 to
June 2014, we find that a two-component mixture of t distribution is a good candidate to describe the shape of the KOSPI return from unconditional and conditional perspectives. Empirical results suggest that the equality assumption on the shape parameter of t distribution yields better discrimination of heterogeneity component in return data. We report the strong regime-dependent characteristics in volatility dynamics with high persistence and asymmetry by employing a regime switching GJR-GARCH model with t innovation model. Compared to two sub-samples, Pre-Crisis (January 2003 ~ December 2007) and Post-Crisis (January 2010 ~ June 2014), we find that the degree of persistence in the Pre-Crisis is higher than in the Post-Crisis along with a strong asymmetry in the low-volatility (high-volatility) regime during the Pre-Crisis (Post-Crisis).
Numerical Iteration for Stationary Probabilities of Markov Chains
Na, Seongryong ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 513~520
DOI : 10.5351/CSAM.2014.21.6.513
We study numerical methods to obtain the stationary probabilities of continuous-time Markov chains whose embedded chains are periodic. The power method is applied to the balance equations of the periodic embedded Markov chains. The power method can have the convergence speed of exponential rate that is ambiguous in its application to original continuous-time Markov chains since the embedded chains are discrete-time processes. An illustrative example is presented to investigate the numerical iteration of this paper. A numerical study shows that a rapid and stable solution for stationary probabilities can be achieved regardless of periodicity and initial conditions.
Bayesian Inference of the Stochastic Gompertz Growth Model for Tumor Growth
Paek, Jayeong ; Choi, Ilsu ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 521~528
DOI : 10.5351/CSAM.2014.21.6.521
A stochastic Gompertz diffusion model for tumor growth is a topic of active interest as cancer is a leading cause of death in Korea. The direct maximum likelihood estimation of stochastic differential equations would be possible based on the continuous path likelihood on condition that a continuous sample path of the process is recorded over the interval. This likelihood is useful in providing a basis for the so-called continuous record or infill likelihood function and infill asymptotic. In practice, we do not have fully continuous data except a few special cases. As a result, the exact ML method is not applicable. In this paper we proposed a method of parameter estimation of stochastic Gompertz differential equation via Markov chain Monte Carlo methods that is applicable for several data structures. We compared a Markov transition data structure with a data structure that have an initial point.
Estimation for Mean and Standard Deviation of Normal Distribution under Type II Censoring
Kim, Namhyun ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 529~538
DOI : 10.5351/CSAM.2014.21.6.529
In this paper, we consider maximum likelihood estimators of normal distribution based on type II censoring. Gupta (1952) and Cohen (1959, 1961) required a table for an auxiliary function to compute since they did not have an explicit form; however, we derive an explicit form for the estimators using a method to approximate the likelihood function. The derived estimators are a special case of Balakrishnan et al. (2003). We compare the estimators with the Gupta's linear estimators through simulation. Gupta's linear estimators are unbiased and easily calculated; subsequently, the proposed estimators have better performance for mean squared errors and variances, although they show bigger biases especially when the ratio of the complete data is small.
A View on the Validity of Central Limit Theorem: An Empirical Study Using Random Samples from Uniform Distribution
Lee, Chanmi ; Kim, Seungah ; Jeong, Jaesik ;
Communications for Statistical Applications and Methods, volume 21, issue 6, 2014, Pages 539~559
DOI : 10.5351/CSAM.2014.21.6.539
We derive the exact distribution of summation for random samples from uniform distribution and then compare the exact distribution with the approximated normal distribution obtained by the central limit theorem. To check the similarity between two distributions, we consider five existing normality tests based on the difference between the target normal distribution and empirical distribution: Anderson-Darling test, Kolmogorov-Smirnov test, Cramer-von Mises test, Shapiro-Wilk test and Shaprio-Francia test. For the purpose of comparison, those normality tests are applied to the simulated data. It can sometimes be difficult to derive an exact distribution. Thus, we try two different transformations to find out which transform is easier to get the exact distribution in terms of calculation complexity. We compare two transformations and comment on the advantages and disadvantages for each transformation.