• 대한토목학회논문집
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• 제31권4A호
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• pp.323-329
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• 2011

대부분의 콘크리트구조설계기준은 구조물의 안전에 대한 여유를 확보하기 위해 하중계수 및 저항계수의 안전계수를 고려하고 있다. 이 안전계수는 하중과 저항의 통계적 불확실성을 적절하게 고려한 구조신뢰성 이론에 근거하여 결정되어야 하는데, 구조신뢰성 이론의 적용은 하중 및 저항에 대한 통계적 모델의 정립이 선행되어야 한다. 이 논문에서는 콘크리트 압축강도, 철근 항복강도 및 부재 단면치수의 통계적 변동성을 고려한 철근콘크리트 부재의 저항모델을 개발하였다. 통계모델 개발에 적용된 자료는 국내의 실험 및 시험 자료를 기초로 하였으며, 몬테칼로 시뮬레이션(Monte Carlo Simulation)기법을 적용하였다. 이 논문의 결과는 콘크리트 구조설계 기준의 검증 및 개정작업에 유용한 자료를 제공할 것으로 기대된다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권3_4호
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• pp.358-371
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• 2003

자동 띄어쓰기는 문장 내에서 잘못 띄어쓴 어절들을 올바르게 복원하는 과정으로서, 독자에게 글의 가독성을 높이고 문장의 뜻을 정확히 전달하기 위해 매우 중요하다. 기존의 통계 기반 자동 띄어쓰기 접근 방법들은 이전 띄어쓰기 상태를 고려하지 않기 때문에 잘못된 확률 정보에 의한 띄어쓰기를 할 수밖에 없었다. 본 논문에서는 기존의 통계 기반 접근 방법 의 문제점을 해결할 수 있는 두 가지 통계적 띄어쓰기 모델을 제안한다. 제안하는 모델은 자동 띄어쓰기를 품사 부착과 같은 분류 문제(classification problem)로 간주할 수 있다는 착안에 기반하며, 은닉 마르코프 모델을 일반화함으로써 확장된 문맥을 고려할 수 있고 보다 정확한 확률을 추정할 수 있도록 고안되었다. 제안하는 모델과 지금까지 가장 좋은 성능을 보이는 기존의 방법을 비교하기 위해 여러 가지 실험 조건에 따른 다양한 실험을 수행하였고, 오류에 대한 자세한 분석을 제시하고 있다 제안하는 모델을 복합 명사를 고려하는 평가 방식에 적응한 실험 결과, 98.33%의 음절 단위 정확도와 93.06%외 어절단위 정확률을 얻었다.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.171-188
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• 2021

Due to an increased demand for longevity risk analysis, various stochastic models have been suggested to evaluate uncertainly in estimated life expectancy and the associated value of future annuity payments. Recently updated data allow us to analyze mortality for a longer historical period and extended age ranges. This study followed up previous case studies using up-to-date empirical data on Korean mortality and the recently developed R package StMoMo for stochastic mortality models analysis. The suitability of stochastic mortality models, focusing on retirement ages, was investigated with goodness-of-fit, validity of models, and ability of generating reasonable sets of simulation paths of future mortality. Comparisons were made across various types of models. Based on the selected models, the variability of important estimated measures associated with pension, annuity, and reverse mortgage were quantified using simulations.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.543-556
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• 2015

We present a survey of contributions that defined the nature and extent of robust statistics for the last 50 years. From the pioneering work of Tukey, Huber, and Hampel that focused on robust location parameter estimation, we presented various generalizations of these estimation procedures that cover a wide variety of models and data analysis methods. Among these extensions, we present linear models, clustered and dependent observations, times series data, binary and discrete data, models for spatial data, nonparametric methods, and forward search methods for outliers. We also present the current interest in robust statistics and conclude with suggestions on the possible future direction of this area for statistical science.

• Korean Journal of Hydrosciences
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• 제4권
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• pp.51-63
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• 1993

This study is to verify the applicability of statistical models in predicting flood frequency at the stage gaging stations of which the flow is under natural condition in the Han River basin. The results of the study show that the statistical flood frequency models were proven to be fairly reasonable to apply in practice, and also were compared with sampling variance to calibrate the statistical efficiency of the estimators of the T year floods Q(T) by two different flood frequency models. As a result, it was showed that for return periods greater than about T = 10 years the annual exceedance series estimators of Q(T) has smaller sampling variance than the annual maximum series estimators. It was showed that for the range of return periods the partial duration series estimators of !(T) has smaller sampling variance than the annual maximum series estimate only if the POT model contains at least 2N(N : record length) items or more in order to estimate Q(T) more efficiently than the ANNMAX model.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Genomics & Informatics
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• 제19권4호
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• pp.36.1-36.11
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• 2021

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

• 대기
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• 제24권4호
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• pp.533-540
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• 2014

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

• Journal of the Korean Statistical Society
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• 제29권1호
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• pp.109-122
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• 2000

An adaptive estimator of the efficient score function for censored and truncated regression models is developed by using $\beta$-splines to estimate the score function and a relatively simple cross validation method to determine the number of knots.