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한국통계학회> CSAM(Communications for Statistical Applications and Methods)

CSAM(Communications for Statistical Applications and Methods) update

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  • : 2287-7843
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  • : 한국통계학회논문집(~2011)→Communications for statistical applications and methods(2012~)

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수록범위 : 1권1호(1994)~29권6호(2022) |수록논문 수 : 2,017
CSAM(Communications for Statistical Applications and Methods)
29권6호(2022년 11월) 수록논문
최근 권호 논문
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SCOPUS

저자 : Seogyoung Lee , Martin Seunghwan Yang , Jongkyeong Kang , Seung Jun Shin

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 629-640 (12 pages)

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Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

SCOPUS

저자 : Seo-young Park , Sunyul Kim , Byungtae Seo

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 641-653 (13 pages)

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Penalized least squares methods are important tools to simultaneously select variables and estimate parameters in linear regression. The penalized maximum likelihood can also be used for the same purpose assuming that the error distribution falls in a certain parametric family of distributions. However, the use of a certain parametric family can suffer a misspecification problem which undermines the estimation accuracy. To give sufficient flexibility to the error distribution, we propose to use the symmetric log-concave error distribution with LASSO penalty. A feasible algorithm to estimate both nonparametric and parametric components in the proposed model is provided. Some numerical studies are also presented showing that the proposed method produces more efficient estimators than some existing methods with similar variable selection performance.

SCOPUS

저자 : Yin Cao , Kwangok Seo , Soohyun Ahn , Johan Lim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 655-664 (10 pages)

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On the motivation by an integrative study of multi-omics data, we are interested in estimating the structure of the sparse cross correlation matrix of two high-dimensional random vectors. We rewrite the problem as a multiple testing problem and propose a new method to estimate the sparse structure of the cross correlation matrix. To do so, we test the correlation coefficients simultaneously and threshold the correlation coefficients by controlling FRD at a predetermined level α. Further, we apply the proposed method and an alternative adaptive thresholding procedure by Cai and Liu (2016) to the integrative analysis of the protein expression data (X) and the mRNA expression data (Y) in TCGA breast cancer cohort. By varying the FDR level α, we show that the new procedure is consistently more efficient in estimating the sparse structure of cross correlation matrix than the alternative one.

SCOPUS

저자 : Young Eun Jeon , Suk-bok Kang , Jung-in Seo

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 665-677 (13 pages)

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This paper proposes a new estimation method based on the maximum product of spacings for estimating unknown parameters of the three-parameter Weibull distribution under a generalized Type-II progressive hybrid censoring scheme which guarantees a constant number of observations and an appropriate experiment duration. The proposed approach is appropriate for a situation where the maximum likelihood estimation is invalid, especially, when the shape parameter is less than unity. Furthermore, it presents the enhanced performance in terms of the bias through the Monte Carlo simulation. In particular, the superiority of this approach is revealed even under the condition where the maximum likelihood estimation satisfies the classical asymptotic properties. Finally, to illustrate the practical application of the proposed approach, the real data analysis is conducted, and the superiority of the proposed method is demonstrated through a simple goodness-of-fit test.

SCOPUS

저자 : Hyunoo Shim , Siok Kim , Yang Ho Choi

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 679-694 (16 pages)

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Mortality risk is a significant threat to individual life, and quantifying the risk is necessary for making a national population plan and is a traditionally fundamental task in the insurance and annuity businesses. Like other advanced countries, the sustainability of life pensions and the management of longevity risks are becoming important in Asian countries entering the era of aging society. In this study, mortality and pension value sustainability trends are compared and analyzed based on national population and mortality data, focusing on four Asian countries from 1990 to 2017. The result of analyzing the robustness and accuracy of generalized linear/nonlinear models reveals that the Cairns-Blake-Dowd model, the nonparametric Renshaw-Haberman model, and the Plat model show low stability. The Currie, CBD M5, M7, and M8 models have high stability against data periods. The M7 and M8 models demonstrate high accuracy. The longevity risk is found to be high in the order of Taiwan, Hong Kong, Korea, and Japan, which is in general inversely related to the population size.

SCOPUS

저자 : Sihyeon Kim , Byeongchan Seong

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 695-708 (14 pages)

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Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying β-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated β time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

SCOPUS

저자 : Hyojeoung Kim , Sujin Park , Sahm Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 709-719 (11 pages)

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Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.

SCOPUS

저자 : Chaeyoung Lee , Jae Keun Yoo

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 6호 발행 연도 : 2022 페이지 : pp. 721-733 (13 pages)

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In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

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