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한국통계학회> CSAM(Communications for Statistical Applications and Methods)

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)~28권1호(2021) |수록논문 수 : 1,929
CSAM(Communications for Statistical Applications and Methods)
28권1호(2021년 01월) 수록논문
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KCI등재

1A new extended alpha power transformed family of distributions: properties, characterizations and an application to a data set in the insurance sciences

저자 : Zubair Ahmad , Eisa Mahmoudi , G. G. Hamedani

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 1호 발행 연도 : 2021 페이지 : pp. 1-19 (19 pages)

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Heavy tailed distributions are useful for modeling actuarial and financial risk management problems. Actuaries often search for finding distributions that provide the best fit to heavy tailed data sets. In the present work, we introduce a new class of heavy tailed distributions of a special sub-model of the proposed family, called a new extended alpha power transformedWeibull distribution, useful for modeling heavy tailed data sets. Mathematical properties along with certain characterizations of the proposed distribution are presented. Maximum likelihood estimates of the model parameters are obtained. A simulation study is provided to evaluate the performance of the maximum likelihood estimators. Actuarial measures such as Value at Risk and Tail Value at Risk are also calculated. Further, a simulation study based on the actuarial measures is done. Finally, an application of the proposed model to a heavy tailed data set is presented. The proposed distribution is compared with some well-known (i) two-parameter models, (ii) three-parameter models and (iii) four-parameter models.

KCI등재

2High-dimensional linear discriminant analysis with moderately clipped LASSO

저자 : Jaeho Chang , Haeseong Moon , Sunghoon Kwon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 1호 발행 연도 : 2021 페이지 : pp. 21-37 (17 pages)

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There is a direct connection between linear discriminant analysis (LDA) and linear regression since the direction vector of the LDA can be obtained by the least square estimation. The connection motivates the penalized LDA when the model is high-dimensional where the number of predictive variables is larger than the sample size. In this paper, we study the penalized LDA for a class of penalties, called the moderately clipped LASSO (MCL), which interpolates between the least absolute shrinkage and selection operator (LASSO) and minimax concave penalty. We prove that the MCL penalized LDA correctly identifies the sparsity of the Bayes direction vector with probability tending to one, which is supported by better finite sample performance than LASSO based on concrete numerical studies.

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3Two-dimensional attention-based multi-input LSTM for time series prediction

저자 : Eun Been Kim , Jung Hoon Park , Yung-seop Lee , Changwon Lim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 1호 발행 연도 : 2021 페이지 : pp. 39-57 (19 pages)

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Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

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4Value at Risk of portfolios using copulas

저자 : Kiwoong Byun , Seongjoo Song

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 1호 발행 연도 : 2021 페이지 : pp. 59-79 (21 pages)

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Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

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5Least quantile squares method for the detection of outliers

저자 : Han Son Seo , Min Yoon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 1호 발행 연도 : 2021 페이지 : pp. 81-88 (8 pages)

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k-least quantile of squares (k-LQS) estimates are a generalization of least median of squares (LMS) estimates. They have not been used as much as LMS because their breakdown points become small as k increases. But if the size of outliers is assumed to be fixed LQS estimates yield a good fit to the majority of data and residuals calculated from LQS estimates can be a reliable tool to detect outliers. We propose to use LQS estimates for separating a clean set from the data in the context of outlyingness of the cases. Three procedures are suggested for the identification of outliers using LQS estimates. Examples are provided to illustrate the methods. A Monte Carlo study show that proposed methods are effective.

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6Binary classification on compositional data

저자 : Jae Yun Joo , Seokho Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 1호 발행 연도 : 2021 페이지 : pp. 89-97 (9 pages)

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Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

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