• Title/Summary/Keyword: 복합 포아송 위험 모형

Search Result 3, Processing Time 0.016 seconds

Ruin Probability in a Compound Poisson Risk Model with a Two-Step Premium Rule (이단계 보험요율의 복합 포아송 위험 모형의 파산 확률)

  • Song, Mi-Jung;Lee, Ji-Yeon
    • Communications for Statistical Applications and Methods
    • /
    • v.18 no.4
    • /
    • pp.433-443
    • /
    • 2011
  • We consider a compound Poisson risk model in which the premiums may depend on the state of the surplus process. By using the overflow probability of the workload process in the corresponding M/G/1 queueing model, we obtain the probability that the ruin occurs before the surplus reaches a given large value in the risk model. We also examplify the ruin probability in case of exponential claims.

The Assessing Comparative Study for Statistical Process Control of Software Reliability Model Based on Logarithmic Learning Effects (대수형 학습효과에 근거한 소프트웨어 신뢰모형에 관한 통계적 공정관리 비교 연구)

  • Kim, Kyung-Soo;Kim, Hee-Cheul
    • Journal of Digital Convergence
    • /
    • v.11 no.12
    • /
    • pp.319-326
    • /
    • 2013
  • There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of logarithmic hazard learning effects property.

IoT Utilization for Predicting the Risk of Circulatory System Diseases and Medical Expenses Due to Short-term Carbon Monoxide Exposure (일산화탄소 단기 노출에 따른 순환계통 질환 위험과 진료비용 예측을 위한 IoT 활용 방안)

  • Lee, Sangho;Cho, Kwangmoon
    • Journal of Internet of Things and Convergence
    • /
    • v.6 no.4
    • /
    • pp.7-14
    • /
    • 2020
  • This study analyzed the effect of the number of deaths of circulatory system diseases according to 12-day short-term exposure of carbon monoxide from January 2010 to December 2018, and predicted the future treatment cost of circulatory system diseases according to increased carbon monoxide concentration. Data were extracted from Air Korea of Korea Environment Corporation and Korea Statistical Office, and analyzed using Poisson regression analysis and ARIMA intervention model. For statistical processing, SPSS Ver. 21.0 program was used. The results of the study are as follows. First, as a result of analyzing the relationship between the impact of short-term carbon monoxide exposure on death of circulatory system diseases from the day to the previous 11 days, it was found that the previous 11 days had the highest impact. Second, with the increase in carbon monoxide concentration, the future circulatory system disease treatment cost was estimated at 10,123 billion won in 2019, higher than the observed value of 9,443 billion won at the end of December 2018. In addition, when summarized by month, it can be seen that the cost of treatment for circulatory diseases increases from January to December, reflecting seasonal fluctuations. Through such research, the future for a healthy life for all citizens can be realized by distributing various devices and equipment utilizing IoT to preemptively respond to the increase in air pollutants such as carbon monoxide.