• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.34-41
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper develops estimators of the parameters of a linear measurement error variance function based on wi thin-unit sample variaoces. This paper devotes to: (1) define measurement error scale factor $\delta$: (2) develop estimators of the parameters of the 1inear measurement error variance function under stratified multistage sampling design and small error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U S Third National Health and Nutrition Examination Survey(NHANES III)

• Korean Journal of Social Welfare
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• v.66 no.3
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• pp.253-276
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• 2014

The purpose of the present study was to examine psychometric properties of the BSI-18 in Korean adults with mental disorders. This study examined internal consistency, test-retest reliability, convergent validity, and a factor structure of the BSI. The sample consisted of 180 adults with mental disorders, who enrolled in mental hospitals and in a day hospital. Data was analysed using reliability analysis, correlation analysis, and confirmatory factor analysis. Major findings were as follows. The Korean version of the BSI showed good internal consistency and test-retest reliability, as well as excellent convergent validity. The original three-factor structure of the BSI-18, proposed by Derogatis, 2001, fitted to the data. These findings indicate that the BSI-18 is a reliable and valid measure as a psychiatric assessment tool and a treatment outcome measure.

• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.319-326
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• 2009

Since string operations were applied to computational biology area, various data structures and algorithms for computing efficient string operations have been studied. The longest common substring problem is an operation to find the longest matching substring in more than two strings, and maximal repeat of string problem is an operation to find substrings repeated more than once in the given string. These operations are importantly used in the string processing area such as pattern matching and likelihood measurement. In this paper, we present algorithms to compute the longest common substring of two strings and to find the maximal repeat of string using three-dimensional $n{\times}n{\times}n$ processors on RMESH(Reconfigurable MESH). Our algorithms have O(1) time complexity.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.407-426
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• 2003

We consider the missing covariates problem in generalized estimating equations(GEE) model. If the covariate is partially missing, GEE can not be calculated. In this paper, we study the performance of 7 imputation methods to handle missing covariates in GEE models, and the properties of GEE estimators are investigated after missing covariates are imputed for ordinal data of repeated measurements. The 7 imputation methods include i) Naive Deletion ii) Sample Average Imputation iii) Row Average Imputation iv) Cross-wave Regression Imputation v) Carry-over Imputation vi) Bayesian Bootstrap vii) Approximate Bayesian Bootstrap. A Monte-Carlo simulation is used to compare the performance of these methods. For the missing mechanism generating the missing data, we assume ignorable nonresponse. Furthermore, we generate missing covariates with or without considering wave nonresp onse patterns.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.289-294
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• 2015

Linear mixed models are commonly used in the clinical pharmaceutical studies to analyze repeated measures such as the crossover study data of bioequivalence studies. In these models, random effects describe the correlation between repeated outcomes and variance-covariance matrix explain within-subject variabilities. Bioequivalence analysis verifies whether a 90% confidence interval for geometric mean ratio of Cmax and AUC between reference drug and test drug is included in the bioequivalence margin [0.8, 1.25] performed using linear mixed models with period, sequence and treatment effects as fixed and sequence nested subject effects as random. A Levofloxacin study is referred to for an example of real data analysis.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.641-650
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• 2010

This study was conducted to investigate whether Mulligan manual therapy and Physical therapy have effectiveness on the pain and muscle assessment questionnaire in female elders with osteoarthritis of the knee. Thirty subjects were participated in this study. And they were all randomly divided into Mulligan manual therapy and Physical therapy group. To evaluate the effects of Mulligan manual therapy and Physical therapy, subjects were evaluated by using visual analogue scale and muscle assessment questionnaire. The assessment parameters were evaluated before, after 2 weeks, and after 4 weeks treatments. And we received a consent form from Mulligan manual therapy subjects. The results of repeated measures analysis of variance showed that pain, strength, endurance, coordination/balance were significantly improved after than before therapy in Mulligan manual therapy group. So we conclude that Mulligan manual therapy has effectiveness on the pain and muscle assessment questionnaire in female elders with osteoarthritis of the knee.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.92-96
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• 2004

본 연구에서는 지하수위 변동 분석을 위한 프로그램을 개발하고, 건설교통부에서 설치, 운영하고 있는 169개 국가 지하수 관측소의 264개 지하수관측정에서 측정된 지하수위자료에 적용하였다. 분석결과 암반대수층과 충적층대수층의 평균수위 및 변동양상이 대체적으로 비슷하게 나타났으며, 이는 관측소 설치 지역의 대부분에서 충적층(10m 내외)과 암반층(70m 내외)이 수리적으로 연결되어 있다는 것을 시사한다. 6시간 간격의 지하수위 관측 자료를 이용하여 지하수위가 상승하는 횟수, 상승량의 합계 산정 등 변동양상을 분석하였다. 분석 결과 해양 및 지구 조석의 영향을 받는 관측정의 경우 지하수위 상승 개수가 450개/yr 이상이 대부분이며, 수위 변동량은 0.1 ～ 1m 정도이고, 수위변동 자료를 시계열로 나타내 보면 하루에 약 2번의 상승과 하강을 반복하는 수위변동 형태를 볼 수 있었다. 양수의 영향이 우세한 관측정에서는 수위 상승 개수가 약 360개/yr, 수위 변동량은 1m 이상의 값이 우세하게 나타났다. 지하수위 상승량은 암반/충적 관측정 모든 관측정에서 전반적으로 강수량과의 상관계수가 높았으며, 같은 관측정의 .자료라도 6시간 간격의 관측 자료보다, 12시간 및 24시간 관측 간격으로 분석한 결과에서 상관관계가 더 높게 나타났다. 12시간 및 24시간 관측 간격으로 분석할 경우 조석 및 양수에 의해 발생된 주기적인 지하수위 변동 성분이 제거되면서 강수에 대한 지하수위 반응의 상관도가 높아진 것으로 해석된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.39-39
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• 2019

인구의 도시 집중화로 인하여 다량의 생활용수의 사용에 따라 하천의 자정능력을 초과하여 오염을 유발시키고 있다. 이에 도시하천들의 오염은 점점 심해져 경제적으로 많은 문제를 유발하고 있다. 이러한 하천오염 문제를 과학적으로 대응하기 위해서는 오염물질의 농도 측정 및 데이터 축척을 통한 오염예측이 필수적이라 할 수 있으며, 부산광역시 보건환경정보 공개시스템에서는 하천수질 자동측정망을 설치하여 시간 단위로 오염물질을 측정하고 있다. 그러나 온천천의 하천수질 데이터는 계속 쌓여가고 있는데 이 데이터를 활용해서 하천수질 인자 예측이 거의 이뤄지지 않고 있다. 본 연구에서는 순환신경망 알고리즘을 활용하여 일 단위의 하천수질 인자 예측을 시도하였다. 순환신경망은 인공신경망의 발전된 형태인 시계열 학습에 강한 RNN, LSTM 알고리즘을 활용한 일단위 하천수질 인자 예측을 하고자 하였다. 연구에 앞서 시간 단위로 쌓여있는 데이터를 평균 내어 일 단위로 변경하였고 이 데이터를 가지고 일 단위 하천수질 인자 예측을 진행하였다. 연구에는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 DO, 탁도 등 항목을 예측하였다. 하천오염의 학습과 예측을 위해 대상지로는 부산지역 온천천의 부곡교, 세병교, 이섭교 관측소를 선택하였다. 연구를 위해 DO, 탁도 등 자료 수집은 부산광역시 보건환경정보 공개시스템의 자료를 활용하였다. 모형의 학습을 위해 입력자료로는 하천수질 인자 자료를 이용하였고, 자료의 학습에는 2014년~2017년 4년간의 자료를 학습자료로 사용하였고, 2018년 1년간의 자료는 모형의 검증을 위해 사용하였다. RNN, LSTM 알고리즘을 활용하여 분석 시 은닉층의 개수, 반복시행횟수, sequence length 등의 값을 조절하여 하천수질 인자 예측을 하였다. 모형의 검증을 위해 $R^2$(r square)와 RMSE(root mean square error)을 이용하여 통계분석을 실시하였다.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.817-827
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• 2014

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. This study proposes a maximum a posteriori (MAP) estimation using Point-Jacobian iteration to restore a degraded image. The proposed method assumes a Gaussian additive noise and Markov random field of spatial continuity. The proposed method employs a neighbor window of spoke type which is composed of 8 line windows at the 8 directions, and a boundary adjacency measure of Mahalanobis square distance between center and neighbor pixels. For the evaluation of the proposed method, a pixel-wise classification was used for simulation data using various patterns similar to the structure exhibited in high resolution imagery and an unsupervised segmentation for the remotely-sensed image data of 1 mspatial resolution observed over the north area of Anyang in Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution imagery.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.