Journal of the Korean Statistical Society韩国统计学会杂志
Journal of the Korean Statistical Society(英文缩写 J KOREAN STAT SOC),ISSN 1226-3192,eISSN 2005-2863,中文译名:韩国统计学会杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 0.820 | Q4 |
| 2022 | 0.600 | Q4 |
| 2023 | 0.600 | Q4 |
| 2024 | 0.800 | Q3 |
| 2025 | 0.800 | Q3 |
Journal of the Korean Statistical Society 最新收录文献
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1. A comprehensive estimator for the Fréchet distribution: asymptotical efficiency, and practical applications to health studies.
PMID:日期:2025-03-29The Fréchet distribution is a fundamental tool in extreme value theory, with applications spanning various fields such as life testing, modeling extreme health-related events (such as infant birth weight extremes or rare disease outbreaks), natural disasters, and environmental sciences. Despite its widespread use, existing parameter estimation methods for the Fréchet distribution face significant limitations, including computational inefficiency, instability with large datasets, and restrictions on the parameter space. To address these challenges, we propose an asymptotically efficient, closed-form estimator for the Fréchet distribution. Our estimator overcomes the limitations of existing methods, providing computational speed, stability, and high estimation quality across both small and large samples. Simulation studies demonstrate the superior performance of the proposed method compared to alternative approaches. Its practical utility is illustrated through its application to the National Center for Health Statistics birth dataset. To further support its adoption, an R package has been developed to enable seamless integration of the method into diverse research applications.
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2. A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak.
PMID:日期:2023-05-29We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order for chains consisting of possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, , remarkably lower than required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial-temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.
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3. Monitoring parameter change for bivariate time series models of counts.
PMID:日期:2023-05-07In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum (CUSUM) process constructed from the (standardized) residuals obtained from those models. To attain control limits, we develop limit theorems for the proposed monitoring process. A simulation study and real data analysis are conducted to affirm the validity of the proposed method.
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4. Goodness of fit test for uniform distribution with censored observation.
PMID:日期:2023-01-23We develop new goodness of fit test for uniform distribution based on a conditional moment characterization. We study the asymptotic properties of the proposed test statistic. We also present a goodness of fit test for uniform distribution to incorporate the right censored observations and studied its properties. A Monte Carlo simulation study is carried out to evaluate the finite sample performance of the proposed tests. We illustrate the test procedures using real data sets.
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5. Empirical likelihood for spatial dynamic panel data models.
PMID:日期:2021-09-29Spatial dynamic panel data (SDPD) models have received great attention in economics in recent 10 years. Existing approaches for the estimation and test of SDPD models are quasi-maximum likelihood (QML) approach and generalized method of moments (GMM). In this article, we introduce the empirical likelihood (EL) method to the statistical inference for SDPD models. The EL ratio statistics are constructed for the parameters of spatial dynamic panel data models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models. Simulation results show that the EL based confidence regions outperform the normal approximation based confidence regions.
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6. Seroprevalence of SARS-CoV-2 antibodies in South Korea.
PMID:日期:2021-05-24In 2020, Korea Disease Control and Prevention Agency reported three rounds of surveys on seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in South Korea. SARS-CoV-2 is the virus which inflicts the coronavirus disease 2019 (COVID-19). We analyze the seroprevalence surveys using a Bayesian method with an informative prior distribution on the seroprevalence parameter, and the sensitivity and specificity of the diagnostic test. We construct the informative prior of the sensitivity and specificity of the diagnostic test using the posterior distribution obtained from the clinical evaluation data. The constraint of the seroprevalence parameter induced from the known confirmed coronavirus 2019 cases can be imposed naturally in the proposed Bayesian model. We also prove that the confidence interval of the seroprevalence parameter based on the Rao's test can be the empty set, while the Bayesian method renders interval estimators with coverage probability close to the nominal level. As of the 30th of October 2020, the credible interval of the estimated SARS-CoV-2 positive population does not exceed 318, 685, approximately of the Korean population. The online version contains supplementary material available at 10.1007/s42952-021-00131-7.
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7. Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection-rejoinder.
PMID:日期:2020-09-16Many existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is "Wild Binary Segmentation 2" (WBS2), a recursive algorithm for producing what we call a 'complete' solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing change-points, where is the data length. The other ingredient is a new model selection procedure, referred to as "Steepest Drop to Low Levels" (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter.
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8. Inflated Density Ratio and Its Variation and Generalization for Computing Marginal Likelihoods.
PMID:日期:2020-01-01In the Bayesian framework, the marginal likelihood plays an important role in variable selection and model comparison. The marginal likelihood is the marginal density of the data after integrating out the parameters over the parameter space. However, this quantity is often analytically intractable due to the complexity of the model. In this paper, we first examine the properties of the inflated density ratio (IDR) method, which is a Monte Carlo method for computing the marginal likelihood using a single MC or Markov chain Monte Carlo (MCMC) sample. We then develop a variation of the IDR estimator, called the dimension reduced inflated density ratio (Dr.IDR) estimator. We further propose a more general identity and then obtain a general dimension reduced (GDr) estimator. Simulation studies are conducted to examine empirical performance of the IDR estimator as well as the Dr.IDR and GDr estimators. We further demonstrate the usefulness of the GDr estimator for computing the normalizing constants in a case study on the inequality-constrained analysis of variance.
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10. A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models.
PMID:日期:2019-05-17Nowadays, Bayesian methods are routinely used for estimating parameters of item response theory (IRT) models. However, the marginal likelihoods are still rarely used for comparing IRT models due to their complexity and a relatively high dimension of the model parameters. In this paper, we review Monte Carlo (MC) methods developed in the literature in recent years and provide a detailed development of how these methods are applied to the IRT models. In particular, we focus on the "best possible" implementation of these MC methods for the IRT models. These MC methods are used to compute the marginal likelihoods under the one-parameter IRT model with the logistic link (1PL model) and the two-parameter logistic IRT model (2PL model) for a real English Examination dataset. We further use the widely applicable information criterion (WAIC) and deviance information criterion (DIC) to compare the 1PL model and the 2PL model. The 2PL model is favored by all of these three Bayesian model comparison criteria for the English Examination data.