• 가정과삶의질연구
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• 제10권1호통권19호
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.

• 대한수학회논문집
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• 제20권1호
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• pp.145-159
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• 2005

In this paper we consider the problem of testing statistical hypotheses for unknown parameters in nonlinear regression models and propose three asymptotically equivalent tests based on regression quantiles estimators, which are Wald test, Lagrange Multiplier test and Likelihood Ratio test. We also derive the asymptotic distributions of the three test statistics both under the null hypotheses and under a sequence of local alternatives and verify that the asymptotic relative efficiency of the proposed test statistics with classical test based on least squares depends on the error distributions of the regression models. We give some examples to illustrate that the test based on the regression quantiles estimators performs better than the test based on the least squares estimators of the least absolute deviation estimators when the disturbance has asymmetric and heavy-tailed distribution.

• 한국정보시스템학회지:정보시스템연구
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• 제4권
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• pp.27-39
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• 1995

This study tests the fuzzy linear regression model to see if there is a performance difference between it and the classical linear regression model. These results show that FLR was better as f forecasting technique when compared with CLR. Another important find in the test of the two different regression methods is that they generate two different predicted P/E ratios from expected value test, variance test and error test of two different regressions, though we can not see a significant difference between two regression models doing test in error measurements (GMRAE, MAPE, MSE, MAD). So, in this financial setting we can conclude that FLR is not superior to CLR, comparing and testing between the t재 different regression models. However, FLR is better than CLR in the error measurements.

• 한국정보과학회논문지:소프트웨어및응용
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• 제28권10호
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• pp.732-742
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• 2001

컴퓨터 분야에서 에이전트의 개념의 전자 상거래, 정보 검색과 같은 많은 어플리케이션에 응용되어 중요시되고 있지만, 소프트웨어 테스트 분야에 에이전트의 개념이 적용되는 것은 드문 일이었다. 테스트 에이전트 시트템 (TAS)은 에이전트 개념을 소프트웨어 테스트 분야에 적용한 새로운 도구로, 'User Interface Agent', 'Test Case Selection & Testing Agent'그리고'Regression Test Agent'로 구성되어 있다. 이들 세개의 에이전트들은 각각 지능성을 나타내는 규칙들을 가지고 객체 지향 프로세스를 딸라 자율적으로 테스트를 진행한다. 이 시스템은 두 가지 측면에서 장점을 가지고 있다. 첫째는 자율적으로 테스트 진행시켜 테스터의 간섭을 최소화한다는 것이고 둘째는 지능적으로 중복이 없고 일관성이 있는 효율적인 테스트케이스를 선택하여 테스트 시간을 감소시키면서 오류검출능력은 향상된다는 것이다. 본 논문에서는 사례를 중심으로 실행과정을 기술하여 TAS를 구성하는 세 개의 에이전트들의 자율적인 행동으로 테스트가 진행되는 것을 보여 TAS가 테스트의 간섭을 최소화한다는 것을 보인다. 그리고 4가지유형의 실험을 수행하여 테스트 시간의 단축과 오류 검출 효과향상을 기술한다.

• Journal of Information Processing Systems
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• 제6권4호
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• pp.597-608
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• 2010

Software evolution is an ongoing process carried out with the aim of extending base applications either for adding new functionalities or for adapting software to changing environments. This brings about the need for estimating and determining the overall impact of changes to a software system. In the last few decades many such change/impact analysis techniques have been developed to identify consequences of making changes to software systems. In this paper we propose a new approach of estimating change/impact analysis by classifying change based on type of change classification e.g. (a) nature and (b) extent of change propagation. The impact set produced consists of two dimensions of information: (a) statements affected by change propagation and (b) percentage i.e. statements affected in each category and involving the overall system. We also propose an algorithm for classifying the type of change. To establish confidence in effectiveness and efficiency we illustrate this technique with the help of an example. Results of our analysis are promising towards achieving the aim of the proposed endeavor to enhance change classification. The proposed dynamic technique for estimating impact sets and their percentage of impact will help software maintainers in performing selective regression testing by analyzing impact sets regarding the nature of change and change dependency.

• Journal of Medicine and Life Science
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• 제20권3호
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• pp.107-114
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• 2023

Epidemiological control of coronavirus disease 2019 (COVID-19) is needed to estimate the infection period of confirmed cases and identify potential cases. The present study, targeting confirmed cases for which the time of COVID-19 symptom onset was disclosed, aimed to investigate the relationship between intervals (day) from symptom onset to testing the cycle threshold (CT) values of real-time reverse transcription-polymerase chain reaction. Of the COVID-19 confirmed cases, those for which the date of suspected symptom onset in the epidemiological investigation was specifically disclosed were included in this study. Interval was defined as the number of days from symptom onset (as disclosed by the patient) to specimen collection for testing. A locally weighted regression smoothing (LOWESS) curve was applied, with intervals as explanatory variables and CT values (CTR for RdRp gene and CTE for E gene) as outcome variables. After finding its non-linear relationship, a polynomial regression model was applied to estimate the 95% confidence interval values of CTR and CTE by interval. The application of LOWESS in 331 patients identified a U-shaped curve relationship between the CTR and CTE values according to the number of interval days, and both CTR and CTE satisfied the quadratic model for interval days. Active application of these results to epidemiological investigations would minimize the chance of failing to identify individuals who are in contact with COVID-19 confirmed cases, thereby reducing the potential transmission of the virus to local communities.

• 비파괴검사학회지
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• 제11권2호
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• pp.22-31
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• 1991

Various types of ultrasonic techniques have been used for the estimation of compressive strength of concrete structures. However, conventional ultrasonic velocity method using only longitudial wave cannot be determined the compressive strength of concrete structures with accuracy. In this paper, by using the introduction of multiple parameter, e. g. velocity of shear wave, velocity of longitudinal wave, attenuation coefficient of shear wave, attenuation coefficient of longitudinal wave, combination condition, age and preservation method, multiple regression analysis method was applied to the determination of compressive strength of concrete structures. The experimental results show that velocity of shear wave can be estimated compressive strength of concrete with more accuracy compared with the velocity of longitudinal wave, accuracy of estimated error range of compressive strength of concrete structures can be enhanced within the range of ${\pm}$10% approximately.

• 응용통계연구
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• 제17권3호
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• pp.547-556
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• 2004

본 논문에서는 불균형 패널회귀모형에서 장애모수가 존재하는 경우 오차성분의 존재유무를 검정하기 위하여 Lagrange Multiplier 검정통계량을 제안하였다 이러한 LM통계량은 그룹변환에 대한 불변성의 성질을 이용하여 유도된 통계량으로 모의실험 결과, 제안된 LM검정은 명목유의수준을 잘 유지하고 있는 것으로 나타났으며 LR검정에 비하여 검정력에 있어서도 높게 나타났다.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1053-1066
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• 2005

In this paper we consider the problem of identifying and testing outliers in linear regression. First we consider the use of the so-called scale ratio tests for testing the null hypothesis of no outliers. This test is based on the ratio of two residual scale estimates. We show the asymptotic distribution of the test statistics and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure using the suggested test is proposed. The new method is compared with classical procedure in the real data example. Unlike other forward procedures, the present one is unaffected by masking and swamping effects because the test statistic is based on robust scale estimate.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.197-212
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• 2002

When assessing goodness-of-fit of a model, a small subset of deviating observations can give rise to a significant lack of fit. It is therefore important to identify such observations and to assess their effects on various aspects of analysis. A Cook's distance measure is usually used to detect influential observation. But it sometimes is not fully effective in identifying truly influential set of observations because there may exist masking or swamping effects. In this paper we confine our attention to influential subset In GLMs such as logistic regression models and loglinear models. We modify a backwards stepping algorithm, which was originally suggested for detecting outlying cells in contingency tables, to detect influential observations in GLMs. The algorithm consists of two steps, the identification step and the testing step. In identification step we Identify influential observations based on influencial measures such as Cook's distances. On the other hand in testing step we test the subset of identified observations to be significant or not Finally we explain the proposed method through two types of dataset related to logistic regression model and loglinear model, respectively.