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CSAM(Communications for Statistical Applications and Methods) update

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  • : 한국통계학회논문집(~2011)→Communications for statistical applications and methods(2012~)

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수록범위 : 1권1호(1994)~25권2호(2018) |수록논문 수 : 1,794
CSAM(Communications for Statistical Applications and Methods)
25권2호(2018년 03월) 수록논문
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1Multivariate measures of skewness for the scale mixtures of skew-normal distributions

저자 : Hyoung-moon Kim , Jun Zhaoa

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 109-130 (22 pages)

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Several measures of multivariate skewness for scale mixtures of skew-normal distributions are derived. As a special case, those of multivariate skew-t distribution are considered in detail. Furthermore, the similarities, differences, and behavior of these measures are explored for cases of some specific members of the multivariate skew-normal and skew-t distributions using a simulation study. Since some measures are vectors, it is better to take all measures in the same scale when comparing them. In order to attain such a set of comparable indices, the sample version is considered for each of the skewness measures that are taken as test statistics for the hypothesis of t distribution against skew-t distribution. An application is reported for the data set consisting of 71 total glycerol and magnesium contents in Grignolino wine.

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2Event date model: a robust Bayesian tool for chronology building

저자 : Lanos Philippea , Philippe Anne

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 131-157 (27 pages)

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We propose a robust event date model to estimate the date of a target event by a combination of individual dates obtained from archaeological artifacts assumed to be contemporaneous. These dates are affected by errors of different types: laboratory and calibration curve errors, irreducible errors related to contaminations, and taphonomic disturbances, hence the possible presence of outliers. Modeling based on a hierarchical Bayesian statistical approach provides a simple way to automatically penalize outlying data without having to remove them from the dataset. Prior information on individual irreducible errors is introduced using a uniform shrinkage density with minimal assumptions about Bayesian parameters. We show that the event date model is more robust than models implemented in BCal or OxCal, although it generally yields less precise credibility intervals. The model is extended in the case of stratigraphic sequences that involve several events with temporal order constraints (relative dating), or with duration, hiatus constraints. Calculations are based on Markov chain Monte Carlo (MCMC) numerical techniques and can be performed using ChronoModel software which is freeware, open source and cross-platform. Features of the software are presented in Vibet et al. (ChronoModel v1.5 user's manual, 2016). We finally compare our prior on event dates implemented in the ChronoModel with the prior in BCal and OxCal which involves supplementary parameters defined as boundaries to phases or sequences.

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3Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation

저자 : Hanna Yoo , Jae Won Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 159-172 (14 pages)

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In many epidemiological studies, missing values in the outcome arise due to censoring. Such censoring is what makes survival analysis special and differentiated from other analytical methods. There are many methods that deal with censored data in survival analysis. However, few studies have dealt with missing covariates in survival data. Furthermore, studies dealing with missing covariates are rare when data are clustered. In this paper, we conducted a simulation study to compare results of several missing data methods when data had clustered multi-structured type with missing covariates. In this study, we modeled unknown baseline hazard and frailty with Bayesian B-Spline to obtain more smooth and accurate estimates. We also used prior information to achieve more accurate results. We assumed the missing mechanism as MAR. We compared the performance of five different missing data techniques and compared these results through simulation studies. We also presented results from a Multi-Center study of Korean IBD patients with Crohn's disease (Lee et al., Journal of the Korean Society of Coloproctology, 28, 188-194, 2012).

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4Weighted zero-inflated Poisson mixed model with an application to Medicaid utilization data

저자 : Sang Mee Lee , Theodore Karrison , Robert S. Nocon , Elbert Huang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 173-184 (12 pages)

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In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

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5A case study for intercontinental comparison of herd behavior in global stock markets

저자 : Woojoo Leea , Yang Ho Choi , Changki Kim , Jae Youn Ahn

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 185-197 (13 pages)

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Measuring market fear is an important way of understanding fundamental economic phenomena related to financial crises. There have been several approaches to measure market fear or panic level in a financial market. Recently, herd behavior has gained its popularity as important economic phenomena explaining the fear in the financial market. In this paper, we investigate herd behavior in global stock markets with a focus on intercontinental comparison. While various risk measures are available for the detection of herd behavior in the market, we use the standardized herd behavior index in Dhaene et al. (Insurance: Mathematics and Economics, 50, 357-370, 2012b) and Lee and Ahn (Dependence Modeling, 5, 316-329, 2017) for the comparison of herd behaviors in global stock markets. A global stock market data from Morgan Stanley Capital International is used to study herd behavior especially during periods of financial crises.

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6Forecasting daily PM10 concentrations in Seoul using various data mining techniques

저자 : Ji-eun Choi , Hyesun Lee , Jongwoo Song

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 199-215 (17 pages)

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Interest in PM10 concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day PM10 concentration based on the principal elements of air pollution, weather information and Beijing PM2.5. If we can forecast the next day PM10 concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean PM10, a daily max PM10 and four stages of PM10 provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily PM10, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the PM10 concentration forecast and the linear regression and Randomforest model performs the best in the PM10 class forecast. The results also indicate that the PM10 in Seoul is influenced by Beijing PM2.5 and air pollution from power stations in the west coast.

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7Construction of bivariate asymmetric copulas

저자 : Saikat Mukherjee , Youngsaeng Lee , Jong-min Kim , Jun Jang ,

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 217-234 (18 pages)

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Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

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8Effect of outliers on the variable selection by the regularized regression

저자 : Junho Jeong , Choongrak Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 2호 발행 연도 : 2018 페이지 : pp. 235-243 (9 pages)

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Many studies exist on the influence of one or few observations on estimators in a variety of statistical models under the “large n, small p” setup; however, diagnostic issues in the regression models have been rarely studied in a high dimensional setup. In the high dimensional data, the influence of observations is more serious because the sample size n is significantly less than the number variables p. Here, we investigate the influence of observations on the least absolute shrinkage and selection operator (LASSO) estimates, suggested by Tibshirani (Journal of the Royal Statistical Society, Series B, 73, 273-282, 1996), and the influence of observations on selected variables by the LASSO in the high dimensional setup. We also derived an analytic expression for the influence of the k observation on LASSO estimates in simple linear regression. Numerical studies based on artificial data and real data are done for illustration. Numerical results showed that the influence of observations on the LASSO estimates and the selected variables by the LASSO in the high dimensional setup is more severe than that in the usual “large n, small p” setup.

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