• 응용통계연구
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• 제22권5호
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• pp.1073-1084
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• 2009

두 평정자가 R개의 순서형 반응 범주로 각 개체를 분류한 $R{\times}R$ 분할표에 대해, 불합치의 정도를 가중치로 부여한 가중 합치도 $H_{\omega}$를 제안하고, 최대 우도추정량 및 분산을 유도하였다. 또한 $2{\times}2$ 분할표에서 Feinstein과 Cicchetti(1990)가 제기한 마지막 역설을 새롭게 정의하고 증명하였으며, ${\kappa}$의 새로운 역설을 제기하고, ${\kappa}$와 주변분포의 전반적인 관계를 정리하였다.

• 한국학교ㆍ지역보건교육학회지
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• 제15권2호
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• pp.91-101
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• 2014

Objectives: This study aimed to identify agreement between smartphone addiction (SA) and perceived SA among adolescents. Methods: This survey was done with 394 subjects conveniently sampled from elementary school (ES), middle school (MS), academic (AHS) and vocational (VHS) high school. The data were collected from June 20 to July 20, 2013 with self-administered questionnaire and analyzed by descriptive statistics, chi-square test and Cohen's kappa (${\kappa}$). Results: High risk of SA showed 2.8% in total, 1.0% in ES, 2.1% in MS, 4.1% in AHS, and 4.0% in VHS, which was statistically different by type of school (p<0.001). Perceived SA showed 22.6% in total, 9.0% in ES, 21.9% in MS, 33.7% in AHS, and 26.0% in VHS, which was statistically different by type of school (p=0.003). The agreement between SA and perceived SA was 82.0% (${\kappa}$=0.54) in total, 91.8% (${\kappa}$=0.53) in ES, 75.0% (${\kappa}$=0.33) in MS, 77.5% (${\kappa}$=0.53) in AHS, and 84.8% (${\kappa}$=0.65) in VHS. Conclusion: The agreement between SA and perceived SA showed moderate in general, but fair in MS. It is suggested to develop step-by-step activities to reduce the gap between SA and perceived SA among adolescen, in particular, middle school students.

• 응용통계연구
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• 제31권3호
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• pp.417-426
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• 2018

최근 동물실험의 대체방법 중 하나로 쥐의 줄기세포 유래 배상체를 이용하여 독성을 시험하는 방법이 개발되었다. 이는 동물에 직접 약물을 주입하는 것이 아닌 배상체 세포에 약물을 투입하여 세포의 변화에 따른 측정값들을 얻는 방법이다. 본 연구에서는 다범주 세포독성 자료를 이용해 통계적 기법인 판별분석(discriminant analysis)과 머신러닝 기법인 서포트 벡터 머신(support vector machine), 인공신경망(artificial neural network), k-인접이웃분류(k-nearest neighbor)의 성능을 비교하였다. 알고리즘의 성능은 분류 정확도(accuracy)와 가중카파계수(weighted Cohen's kappa coefficient)로 비교하였다.

• Communications for Statistical Applications and Methods
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• 제5권2호
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• pp.277-285
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• 1998

The smoothing parameter $\lambda$ in smoothing spline regression is usually selected by minimizing cross-validation (CV) or generalized cross-validation (GCV). But, simple CV or GCV is poor candidate for estimating prediction error. We defined MGCV (Multifold Generalized Cross-validation) as a criterion for selecting smoothing parameter in smoothing spline regression. This is a version of cross-validation using $leave-\kappa-out$ method. Some numerical results comparing MGCV and GCV are done.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.285-294
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• 2005

This paper deals with the comparison of parameter estimation methods in a 3-parameter Kappa distribution which is sometimes used in flood frequency analysis. Method of moment estimation(MME), L-moment estimation(L-ME), and maximum likelihood estimation(MLE) are applied to estimate three parameters. The performance of these methods are compared by Monte-carlo simulations. Especially for computing MME and L-ME, three dimensional nonlinear equations are simplified to one dimensional equation which is calculated by the Newton-Raphson iteration under constraint. Based on the criterion of the mean squared error, L-ME (or MME) is recommended to use for small sample size( n$\le$100) while MLE is good for large sample size.

• 대한원격탐사학회지
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• 제25권3호
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• pp.233-242
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• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• 한국정보통신학회논문지
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• 제11권12호
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• pp.2311-2318
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• 2007

유한고장수를 가진 비동질적인 포아송 과정에 기초한 모형들에서 잔존 오류 1개당 고장 발생률은 일반적으로 상수, 혹은 단조증가 및 단조 감소 추세를 가지고 있다. 소프트웨어 제품의 정확한 인도시기를 예측하거나 효용성 및 신뢰성을 예측하기 위해서는 소프트웨어 테스팅 과정에서 중요한 요소인 테스트 커버리지를 이용하면 보다 효율적인 테스팅 작업을 할 수 있다. 이런 모형을 ENHPP모형이라고 한다. 본 논문에서는 기존의 소프트웨어 신뢰성 모형인 지수 커버리지 모형과 S-커버리지 모형을 재조명하고 이 분야에 적용될 수 있는 Kappa 분포를 이용한 모형인 Kappa 커버리지 모형을 제안하였다. 고장 간격시간으로 구성된 자료를 이용한 모수추정 방법은 최우추정법과 일반적인 수치해석 방법인 이분법을 사용하여 모수 추정을 실시하고 효율적인 모형 선택은 편차자승합(SSE) 및 콜모고로프 거리를 적용하여 모형들에 대한 효율적인 모형 선택도 시도 하였다. 수치적인 예에서는 실제 고장자료를 통하여 분석하였다 이 자료들에서 기존의 모형과 Kappa 커버리지 모형의 비교를 위하여 산술적 및 라플라스 검정, 편의 검정 등을 이용하였다.

• 대한원격탐사학회지
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• 제23권3호
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• pp.153-160
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• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.

• Journal of Korean Neurosurgical Society
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• 제60권4호
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• pp.465-470
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• 2017

Objective : Magnetic resonance imaging (MRI) grading systems using sagittal images are useful for evaluation of lumbar foraminal stenosis. We evaluated whether such a grading system is useful as a diagnostic tool for surgery. Methods : Between July 2014 and June 2015, 99 consecutive patients underwent unilateral lumbar foraminotomy for lumbar foraminal stenosis. Surgically confirmed foraminal stenosis and the contralateral, asymptomatic neuroforamen were assessed based on a 4-point MRI grading system. Two experienced researchers independently evaluated the MR sagittal images. Interobserver agreement and intraobserver agreement were analyzed using ${\kappa}$ statistics. Results : The mean age of patients (54 women, 45 men) was 62.5 years. A total of 101 levels (202 neuroforamens) were evaluated. MRI grades for operated neuroforamens were as follows : Grade 0 in 0.99%, Grade 1 in 5.28%, Grade 2 in 14.85%, and Grade 3 in 78.88%. Interobserver agreement was moderate for operated neuroforamens (${\kappa}=0.511$) and good for asymptomatic neuroforamens (${\kappa}=0.696$). Intraobserver agreement by reader 1 for operated neuroforamens was good (${\kappa}=0.776$) and that for asymptomatic neuroforamens was very good (${\kappa}=0.831$). In terms of lumbar level, interobserver agreement for L5-S1 (${\kappa}=0.313$, fair) was relatively lower than the other level (${\kappa}=0.804$, very good). Conclusion : MRI grading system for lumbar foraminal stenosis is thought to be useful as a diagnostic tool for surgery in the lumbar spine; however, it is less reliable for symptomatic L5-S1 foraminal stenosis than for other levels. Thus, various clinical factors as well as the MRI grading system are required for surgical decision-making.

• 호남수학학술지
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• 제24권1호
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• pp.131-142
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• 2002

A $1{\frac{1}{2}}$-starcompact space has one of the most curious properties among the spaces of starcompactness. It is not too far away from countably compact spaces and may be considered as the first candidate for extending theorems about countably compact spaces. Unfortunately, $1{\frac{1}{2}}$-starcompactness is not so easy to be recognized as 2-starcompactness which will follow from countable pracompactness. We investigate some properties around $1{\frac{1}{2}}$-starcompact spaces.