간행물

한국통계학회> CSAM(Communications for Statistical Applications and Methods)

CSAM(Communications for Statistical Applications and Methods) update

  • : 한국통계학회
  • : 자연과학분야  >  통계학
  • : KCI등재
  • :
  • : 연속간행물
  • : 격월
  • : 2287-7843
  • : 2383-4757
  • : 한국통계학회논문집(~2011)→Communications for statistical applications and methods(2012~)

수록정보
수록범위 : 1권1호(1994)~28권4호(2021) |수록논문 수 : 1,950
CSAM(Communications for Statistical Applications and Methods)
28권4호(2021년 07월) 수록논문
최근 권호 논문
| | | |

KCI등재

1Generalized Bayes estimation for a SAR model with linear restrictions binding the coefficients

저자 : Anoop Chaturvedi , Sandeep Mishra

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 4호 발행 연도 : 2021 페이지 : pp. 315-327 (13 pages)

다운로드

(기관인증 필요)

초록보기

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

KCI등재

2Is it possible to forecast KOSPI direction using deep learning methods?

저자 : Songa Choi , Jongwoo Song

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 4호 발행 연도 : 2021 페이지 : pp. 329-338 (10 pages)

다운로드

(기관인증 필요)

초록보기

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

KCI등재

3Identification of risk factors and development of the nomogram for delirium

저자 : Min-seok Shin , Ji-eun Jang , Jea-young Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 4호 발행 연도 : 2021 페이지 : pp. 339-350 (12 pages)

다운로드

(기관인증 필요)

초록보기

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.

KCI등재

4An alternative method for estimating lognormal means

저자 : Yeil Kwon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 4호 발행 연도 : 2021 페이지 : pp. 351-368 (18 pages)

다운로드

(기관인증 필요)

초록보기

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ2. The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ2 is small and exhibits a meaningful decrease in the RMSE under moderate and large σ2 values.

KCI등재

5vlda: An R package for statistical visualization of multidimensional longitudinal data

저자 : Bo-hui Lee , Seongwon Ryu , Yong-seok Choi

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 4호 발행 연도 : 2021 페이지 : pp. 369-391 (23 pages)

다운로드

(기관인증 필요)

초록보기

The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.

KCI등재

6On the models for the distribution of examination score for projecting the demand for Korean Long-Term Care Insurance

저자 : Sophia Nicole Javal , Hyuk-sung Kwon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 28권 4호 발행 연도 : 2021 페이지 : pp. 393-409 (17 pages)

다운로드

(기관인증 필요)

초록보기

The Korean Long-Term Care Insurance (K-LTCI) provides financial support for long-term care service to people who need various types of assistance with daily activities. As the number of elderly people in Korea is expected to increase in the future, the demand for long-term care insurance would also increase over time. Projection of future expenditure on K-LTCI depends on the number of beneficiaries within the grading system of K-LTCI based on the test scores of applicants. This study investigated the suitability of mixture distributions to the model K-LTCI score distribution using recent empirical data on K-LTCI, provided by the National Health Insurance Service (NHIS). Based on the developed mixture models, the number of beneficiaries in each grade and its variability under the current grading system were estimated by simulation. It was observed that a mixture model is suitable for K-LTCI score distribution and may prove useful in devising a funding plan for K-LTCI benefit payment and investigating the effects of any possible revision in the K-LTCI grading system.

1
권호별 보기

내가 찾은 최근 검색어

최근 열람 자료

맞춤 논문

보관함

내 보관함
공유한 보관함

1:1문의

닫기