• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.490-494
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• 2008

본 연구에서는 상수도 배수관로의 내 외부적 특성에 따라 개별관로를 정의하는 방법을 연구대상 지역의 배수관로 파손 데이터베이스에 적용하여 비례위험모형을 구축하였다. 연구에 사용된 자료는 연구대상지역의 배수관로의 제원 및 파손시기를 포함하는 관로 파손데이터베이스, 관로매설지역의 급수인구 및 수압범위에 관한 자료를 포함하는 GRID 데이터베이스와 관로매설지역의 토지개발 정도에 관한 자료를 포함한다. 이러한 자료를 이용하여 관로를 순차적 파손경험에 따라 7개의 생존시간군(STG I $\sim$ VII)으로 구분하고 각 생존시간군에 대한 비례위험모형(Model I $\sim$ VII)을 구축하였다. 이러한 모형을 이용하여 관로의 파손횟수가 증가하는 동안 파손에 영향을 미치는 인자의 변화와 그 효과를 파악하였으며, 또한 추정된 공변수의 위험비율을 분석함으로써 관로의 제원 혹은 매설환경, 급수인구 등에 따른 위험률의 상대적인 변화를 분석하였다. 또한 비례 위험모형의 구축과정에서 관로의 파손에 영향을 미치는 공변수의 비례성 가정을 검토하여 시간종속형 공변수를 모형화하였으며, 모형의 이탈잔차(deviance residual)를 분석하여 모형의 적합성을 검토하였다. 본 연구에서 구축된 비례위험모형에 대해 Shoenfeld 잔차를 이용한 스코어 잔차의 변화(score process)를 검토한 결과, Model I 과 Model II 에 대해서는 공변수의 시간종속 효과가 발견되었다. Model I에 대해서는 관로재질과 급수인구의 영향이 시간에 따라 변하며 Model II에서는 급수인구의 영향만이 시간에 따라 변하는 것으로 나타났다. 한편 Model III $\sim$ Model VII 들에 대해서는 공변수의 영향이 시간에 따라 변하지 않는 것으로 나타났다. 각 생존시간군에 대해 관로재질, 토지개발정도, 관로길이 및 급수인구의 변화가 관로의 상대적 누수위험률에 미치는 영향을 상대위험률의 95% 신뢰구간을 고려하여 정량적으로 산정하였고, 시간 종속형 공변수로 모형화된 공변수는 시간에 따른 공변수 영향의 변화를 분석하였다. 순차적 파손사건에 대한 비례위험모형의 구축 결과 생존시간군(STG) I의 기저위험률은 매설 후 대략 450개월까지는 파손 위험률이 '0'에 가까우나 그 이후로 급격히 증가하다가 매설 후 약 700개월에 이르러서는 약간 감소하고 약 850개월 이후에는 다시 급격히 증가한다. STG II의 기저위험률은 첫 번째 파손 후 약 300개월이 되면 위험률이 급격히 증가하는 것으로 나타났다. STG III $\sim$ STG VII의 기저위험률은 이차함수의 형태를 띄며, 특히 STG V, STG VI 및 STG VII의 기저위험률은 욕조형 곡선(bathtub curve)의 형태를 가진다. 각 생존시간군의 기저생존함수의 생존확률 '0.5'에 해당하는 기저중간생존시간에 대한 분석으로부터 파손횟수가 많아질수록 순차적 파손사건 사이의 경과시간은 감소하는 것으로 나타났다. 이러한 기저생존시간에 대한 경향은 관로의 파손횟수가 많아질수록 관로의 일반적은 내구성은 감소하기 때문인 것으로 분석된다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.5
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• pp.63-72
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• 2018

The purpose of this study was to analyze the survival rate and survival characteristics of young start-up entrepreneurs supported with public financing, by using non-parametric statistic of Kaplanr-Meier Analysis on non-financial data. Average survival periods of different survival characteristics have been estimated by dividing the age groups into 20s and 30s. After then, the main variables affecting the survival period have been analyzed. 3,825 firms guaranteed by Credit Guarantee Institutions in Korea were used as database for the analysis. 3,242 firms have survived while 583 firms have gone insolvent. The study period was from January 1, 2011 to December 31, 2017. Age-based breakdown of the business founders show that 3 variables in the 20s and 5 variables in the 30s are derived as the significant variables, resulting in the significant differences of each age group. In other words, the start-up support agencies and financial institutions need to develop a credit evaluation system that distinguishes the criteria of age range and find information that reflect the characteristics of entrepreneurs in their 20s as well as developing tailor-made financial products. Also, step-by-step support measures are required for the start-ups of high survival times and make them grow into promising SMEs. Meanwhile, non-financial support plans shall be invigorated along with the financial ones to help the start-ups of low survival times. This study is meaningful in that the survival analysis has been conducted by using the non-financial data of young start-up entrepreneurs. It is expected that the results of this analysis contribute to the enhancement of survival rate of start-ups by providing start-up support agencies and start-up business owners with the unique information of the survival characteristics.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.111-122
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• 2023

The purpose of this study is to verify the factors affecting survival time by estimating survival rate and survival time using non-financial information of social enterprises using credit guarantee in credit guarantee institutions, and provide information to stakeholders to improve survival rate and employ to contribute to maintaining and expanding the As a research method, survival analysis was performed using a non-parametric analysis method, Kaplan-Meier Analysis. As a sample, 621 companies (577 normal companies, 44 insolvent companies) established between 2009 and 2018 were selected as the target companies. As a result of examining the factors affecting survival time by classifying social enterprise representative information and corporate information, representative credit rating, representative home ownership, credit transaction period, and corporate credit rating were derived as significant variables affecting survival time. In the future, financial institutions will be able to induce corporate soundness by reflecting factors that affect survival when examining loans for social enterprises, contributing to job retention and reduction of social costs. Supporting organizations such as the government and private organizations will be able to use it in various ways, such as policy establishment and education and training for the growth and sustainability of social enterprises. With this study as an opportunity, I hope that research will continue with more interest in the factors influencing social enterprise performance as well as corporate insolvency.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.163-175
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• 1998

The change of hazard rates at some unknown time point has been the interest of many statisticians. But it was restricted to the constant hazard rates which correspond to the exponential distribution. In this paper we generalize the change-point model in which any specific functional forms of hazard rates are net assumed. The assumed model includes various types of changes before and after the unknown time point. The Nelson estimator of cumulative hazard function is introduced. We estimate the change-point maximizing slope changes of Nelson estimator. Consistency and asymptotic distribution of bootstrap estimator are obtained using the martingale theory. Through a Monte Carlo study we check the performance of the proposed method. We also explain the proposed method using the Stanford Heart Transplant Data set.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.231-239
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• 2010

In vaccine research, proportional hazards model including only first event have been widely used for estimating vaccine efficacy because it is easy to interpret and convenient. However, this method causes not only loss of information but also biased result when heterogeneity of study subject in exposure and susceptibility exists. Furthermore, it is hard to ignore the possibility that each event is correlated with each other in the repeated events. Therefore, we compare various statistical models to estimate vaccine efficacy under various situations with heterogeneity and event dependency.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.445-455
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• 2009

In this paper proportional hazards models for the first through seventh break of 150 mm cast iron pipes in a case study area are established. During the modeling process the assumption of the proportional hazards for covariates on the hazards is examined to include the time-dependent covariate terms in the models. As a result, the pipe material/joint type and the number of customers are modeled as time-dependent for the first failure, and for the second failure only the number of customers is modeled as time-dependent. From the analysis on the baseline hazard functions the failure hazards are found to be generally increasing for the first and second failure, while the hazards of the third break and beyond showed a form of a bath-tub. Furthermore, the changes in the baseline hazard rates according to the time and number of break reflect that the general condition of the pipes is deteriorating. The factors causing pipe break and their effects are analyzed based on the estimated regression coefficients and their hazard ratios, and the constructed models are verified using the deviance residuals of the models.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.135-148
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• 2007

Understanding the relation between characteristics of an accident and its duration is crucial for the efficient response of accidents and the reduction of total delay caused by accidents. Thus the objective of this study is to model accident duration using an AFT metric model. Although the log-logistic and log-normal AFT models were selected based on the previous studies and statistical theory, the log-logistic model was better fitted. Since the AFT model is commonly used for the purpose of prediction, the estimated model can be also used for the prediction of duration on freeways as soon as the base accident information is reported. Therefore, the predicted information will be directly useful to make some decisions regarding the resources needed to clear accident and dispatch crews as well as will lead to less traffic congestion and much saving the injured.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.457-464
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• 2009

This paper proposes methodologies for analyzing the accuracy of the proportional hazards model in predicting consecutive break times of water mains and estimating the time interval for economical water main replacement. By using the survival functions that are based on the proportional hazards models a criterion for the prediction of the consecutive pipe breaks is determined so that the prediction errors are minimized. The criterion to predict pipe break times are determined as the survival probability of 0.70 and only the models for the third through the seventh break are analyzed to be reliable for predicting break times for the case study pipes. Subsequently, the criterion and the estimated lower and upper bound survival functions of consecutive breaks are used in predicting the lower and upper bounds of the 95% confidence interval of future break times of an example water main. Two General Pipe Break Prediction Models(GPBMs) are estimated for an example pipe using the two series of recorded and predicted lower and upper bound break times. The threshold break rate is coupled with the two GPBMs and solved for time to obtain the economical replacement time interval.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1225-1238
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• 2015

In this research, we fit various survival models and conduct tests and estimation for the hazard change-point with the rectal cancer data. By the log-rank tests, at significance level ${\alpha}=0.10$, survival functions are significantly different according to the uniporter of glucose (GLUT1), clinical stage (cstage) and pathologic stage (ypstage). From the Cox proportional hazard model, the most significant covariates are GLUT1 and ypstage. Assuming that the rectal cancer data follows the exponential distribution, we estimate one hazard change-point using Matthews and Farewell (1982), Henderson (1990) and Loader (1991) methods.