불균형(不均衡) 일원(一元) 변량모형(變量模型)에서 추정방법(推定方法)에 따른 분산성분(分散成分)의 추정량(推定量)이 음(陰)이 될 확률(確率)의 계산(計算)

송규문,Song, Gyu-Moon 한국데이터정보과학회 1993 한국데이터정보과학회지 Vol.4 No.-

For the One-way random effects model with unbalanced data, the AOV and MINQUE estimates of variance components are frequently found to be negative. The primary objective of present study is placed on the computation of the probability of the main effect variance component, being negative. The probability of negative estimators from AOV and MINQUE can be obtained by theoretical computation under the normality assumption. It is, however, difficult to compute the probability of negative estimates for these estimators under arbitrary distributions, and hence their probabilities of being negative were computed by simulation experiment in this study. It was shown that there was no significant difference between the theoretical probability under normality and calculated probability by simulation experiment, and that probability of negative estimates decreases as sample size, number of levels and the value of increase.

Confidence Interval for the Variance Component in a Unbalanced One-way Random Effects Model

송규문,Song, Gyu-Moon 한국데이터정보과학회 2002 한국데이터정보과학회지 Vol.13 No.2

Two methods are proposed for constructing a confidence interval on the among group variance component in a unbalanced one-way random effects model. Computer simulation is used to compare these methods with alternative procedures. The results indicate that the method1 and methods2 perform well over small group size and large sample size respectively.

A CUSUM Algorithm for Early Detection of Structural Changes in Won/Dollar Exchange Market

송규문,박병춘,강훈규 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.2

This study deals with an early detection problem of structural change in won/dollar exchange market. A CUSUM algorithm is developed to monitor relevant economic variables indicating structural change in won/dollar exchange market. We applied the CUSUM algorithm to examine whether or not it was possible to alarm the 1997 economic crisis of Korea in advance.

R을 이용한 이상점 탐지 알고리즘의 구현

송규문,문지은,박철용,Song, Gyu-Moon,Moon, Ji-Eun,Park, Cheol-Yong 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.3

불법 오물 투기는 정부가 당면한 시급한 문제들 중의 하나이다. 최근 들어 관련기관들은 실시간으로 연속적으로 수질의 상태를 감지 할 수 있는 화학적 산소요구량 자동측정기를 강과 하천 등에 설치하고 있다. 본 논문에서는 시계열 간섭모형을 이용하여 화학적 산소요구량 자동측정기로부터 발생하는 데이터를 분석하여 투기시점이라고 여겨지는 이상점을 탐지하는 알고리즘을 R언어를 이용하여 구현한다. R을 이용한 알고리즘을 통해 단계별 계산에서 수동 작업을 피할 수 있기 때문에 알고리즘의 자동화를 달성할 수 있고, 한 단계 더 나아가 모의실험에서 사용될 수 있을 것이다. Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. Recently government agency installed COD (chemical oxygen demand) auto-monitering machines in river. In this article we provide an outlier detection algorithm using R based on the time series intervention model that detects some outlier values among those COD time series values generated from an auto-monitering machine. Through this algorithm using R, we can achieve an automatic algorithm that does not need manual intervention in each step, and that can further be used in simulation study.

A Study on Error Detection Algorithm of COD Measurement Machine

최현석,송규문,김태윤 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.4

This paper provides a statistical algorithm which detects COD (chemical oxygen demand) measurement machine error on real-time. For this we propose to use regression model fitting and check its validity against the current observations. The main idea is that the normal regression relation between COD measurement and other parameters inside the machine will be violated when the machine is out of order. RR2수정된 R2추정값의 표준오차0.8170.6680.6623.15

불균형 자료 분석과 가설 검정에 관한 연구

장석환,송규문,김장한 한국통계학회 1992 응용통계연구 Vol.5 No.2

불균형 자료 분석에 대해서는 일찍이 Brown(1932)과 Yates(1934)의 연구 이에 Finney(1948), Stevens(1948), Henderson(1953), Kramer(1955)등 많은 사람들이 관심을 가지고 연구하였고 Searle(1971, 1977, 1981)은 R(v) 표기법으로 모형식의 상수적합에 의한 변동을 나타내었으며 Hocking과 Speed(1975), Speed와 Hocking (1976)이 사용한 제한들을 $\Sigma$ -, W-, O-restrictions라고 하였다. 또한 Speed등(1978)은 비가중평균법(method of unweighted means), 평균의 가중제곱법(method of weighted squares of means), 상수적합법 (method of fitting constants), Overall- Spiegel법, Henderson방법 등을 비교설명하고 Burdick 등(1974)은 각 변동을 기하학적으로 해석하려 하였다. 백(1987a,1987b)은 SAS 팩키지에 의한 변동을 설명하였고 장(1988)도 Searle(1977, 1981)의 방법을 이용하여 가설검정과 변동을 검토한바 있다. 본 연구에서는 여러 가지 모형에 대하여 $n_{ij} > 0, 또는 n_{ij} \geq 0$ 인 경우에 변동계산과 W-, $\Sigma$-, O- 제한 조건하에서의 변동과 가설을 재조명해 보고져 한다. In the present study two sets of unbalanced two-way cross-classification data with and without empty cell(s) were used to evaluate empirically the various sums of squares in the analysis of variance table. Searle(1977) and Searle et.al.(1981) developed a method of computing R($\alpha$\mid$\mu, \beta$) and R($\beta$\mid$\mu, \alpha$) by the use of partitioned matrix of X'X for the model of no interaction, interchanging the columns of X in order of $\alpha, \mu, \beta$ and accordingly the elements in b. An alternative way of computing R($\alpha$\mid$\mu, \beta$), R($\beta$\mid$\mu, \alpha$) and R($\gamma$\mid$\mu, \alpha, \beta$) without interchanging the columns of X has been found by means of,$(X'X)^-$ derived, using $W_2 = Z_2Z_2-Z_2Z_1(Z_1Z_1)^-Z_1Z_2$. It is true that $R(\alpha$\mid$\mu,\beta,\gamma)\Sigma = SSA_W and R(\beta$\mid$\mu,\alpha,\gamma)\Sigma = SSB_W$ where $SSA_W$ and means analysis and $R(\gamma$\mid$\mu,\alpha,\beta) = R(\gamma$\mid$\mu,\alpha,\beta)\Sigma$ for the data without empty cell, but not for the data with empty cell(s). It is also noticed that for the datd with empty cells under W - restrictions $R(\alpha$\mid$\mu,\beta,\gamma)_W = R(\mu,\alpha,\beta,\gamma)_W - R(\mu,\alpha,\beta,\gamma)_W = R(\alpha$\mid$\mu) and R(\beta$\mid$\mu,\alpha,\gamma)_W = R(\mu,\alpha,\beta,\gamma)_W - R(\mu,\alpha,\beta,\gamma)_W = R(\beta$\mid$\mu) but R(\gamma$\mid$\mu,\alpha,\beta)_W = R(\mu,\alpha,\beta,\gamma)_W - R(\mu,\alpha,\beta,\gamma)_W \neq R(\gamma$\mid$\mu,\alpha,\beta)$. The hypotheses $H_o : K' b = 0$ commonly tested were examined in the relation with the corresponding sums of squares for $R(\alpha$\mid$\mu), R(\beta$\mid$\mu), R(\alpha$\mid$\mu,\beta), R(\beta$\mid$\mu,\alpha), R(\alpha$\mid$\mu,\beta,\gamma), R(\beta$\mid$\mu,\alpha,\gamma), and R(\gamma$\mid$\mu,\alpha,\beta)$ under the restrictions.

환자이탈군 특성요인과 이탈환자 예측모형에 관한 연구

민경진,송규문,김광환 한국의료QA학회 2002 한국의료질향상학회지 Vol.9 No.1

Background : We developed a model for predicting premature discharge and identifying related factors. Methods : Prediction model was developed by data mining techniques. Basic data were collected from the total discharge data base of a university hospitals in Chungnam province during the period from July 1, 1999 to June 30, 2000. Results : 1. Among 22,873 patients, the number of patients discharge with usual discharge orders were 21,695 or 94.8%. The number of the prematurely discharged patients were 1,178 or 5.2%. 2. The primary reason for unusal discharge was transfer to other hospital. Move to a local hospital closer to their home and burdensome medical expenses were main reasons. 3. Predictability of each model was tested using the top 10 percent of patients with the highest probabilities of premature discharge. The neural network model was chosen as the most appropriate model for predicting prematurely discharged patients. 4. Ten percent of the total number of patients had been selected randomly to test the effectiveness of the neural network model. We have chosen the threshold of the neural network model as 0.7. The number of patients who were expected to discharge prematurely was 312. Among them, 241 had been discharged prematurely(77.2%). Conclusion : of the several data mining techniques used, the neural network model was the most effective, It can be used to identify and manage the patients who are expected to discharge prematurely.

Analysis of Students Leaving Their Majors Using Decision Tree

박철용,송규문,Park, Cheol-Yong,Song, Gyu-Moon 한국데이터정보과학회 2002 한국데이터정보과학회지 Vol.13 No.2

Since 1997, when a new educational system that encourages faculties instead of departments in universities is first introduced, students have much more chance to choose and leave their majors than before. As a result, colleges of basic arts and sciences confront with a serious problem since lots of students have left their majors at the colleges. In this paper, we analyze and provide a predictive model for those students in a university using decision trees.

A study on development of economic instability index

도종두,송규문,김태윤 한국데이터정보과학회 2004 한국데이터정보과학회지 Vol.15 No.2

Kim et al.. (2003) developed an Economic Instability Index (EII) by using mean squared error (MSE) from the neural network (NN) trained on the 1995 KOSPI. In this paper we study validity of the NN. For this we compare the NN with the well known Box-Jenkins linear auto-regressive processes. Our conclusive understanding of the problem is that the NN provides quite effective EII because it tends to overfit.

시계열 간섭 모형을 이용한 불법 오물 투기 실시간 탐지 알고리즘 연구

문지은,송규문,김태윤 한국데이터정보과학회 2010 한국데이터정보과학회지 Vol.21 No.5

Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. One solution to this problem is to find an efficient way of managing and supervising the water quality under various kinds of conditions. In this article we establish WQMA (water quality monitoring algorithm) based on the time series intervention model. It turns out thatWQMA is quite successful in detecting illegal waste dumping. 수질오염의 요인인 불법 오물 투기는 사회적 이슈로 대두되고 있고 관련 감독기관이 해결해야 할 문제들 중의 하나이다. 따라서 불법 오물 투기를 막는 체계적인 관리, 감독이 시급한 상황이다. 이를 위해 최근 들어 관련기관들은 실시간으로 연속적으로 수질의 상태를 감지 할 수 있는 자동측정기를 하천에 설치하고 있다. 본 논문에서는 수질 자동측정기로부터 발생하는 실시간 데이터를 감시하여 이상점을 탐지하게 하는 수질 감시 알고리즘을 제안한다. 특히 수질 자동 측정기로서 흔히 사용되는 화학적 산소요구량 자동측정 장치기를 위한 수질 감시 알고리즘을 개발한다. 본 논문의 수질 감시 알고리즘은 기본적으로 시계열 간섭모형을 활용한다.