• Convergence Security Journal
• /
• v.12 no.6
• /
• pp.85-92
• /
• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. 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). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by 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 log Poission, log-linear and Parto distribution.

• Communications for Statistical Applications and Methods
• /
• v.16 no.5
• /
• pp.803-812
• /
• 2009

The application of extreme value theory to financial data is a fairly recent innovation. The classical annual maximum method is to fit the generalized extreme value distribution to the annual maxima of a data series. An alterative modern method, the so-called threshold method, is to fit the generalized Pareto distribution to the excesses over a high threshold from the data series. A more substantial variant is to take the point-process viewpoint of high-level exceedances. That is, the exceedance times and excess values of a high threshold are viewed as a two-dimensional point process whose limiting form is a non-homogeneous Poisson process. In this paper, we apply the two-dimensional non-homogeneous Poisson process model to daily losses, daily negative log-returns, in the data series of KBW/USD exchange rate, collected from January 4th, 1982 until December 31 st, 2008. The main question is how to estimate extreme quantiles of losses such as the 10-year or 50-year return level.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.5
• /
• pp.345-353
• /
• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do parameter inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision to market software, the conditional failure rate is an important variables. In this case, finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. 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, proposed a control mechanism based on NHPP using mean value function of polynomial hazard function.

• Journal of the Korean Mathematical Society
• /
• v.52 no.3
• /
• pp.447-467
• /
• 2015

In this paper, we consider a multi-risk model based on the policy entrance process with n independent policies. For each policy, the entrance process of the customer is a non-homogeneous Poisson process, and the claim process is a renewal process. The loss process of the single-risk model is a random sum of stochastic processes, and the actual individual claim sizes are described as extended upper negatively dependent (EUND) structure with heavy tails. We derive precise large deviations for the loss process of the multi-risk model after giving the precise large deviations of the single-risk model. Our results extend and improve the existing results in significant ways.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.10 no.2
• /
• pp.41-47
• /
• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.

• Journal of applied mathematics & informatics
• /
• v.14 no.1_2
• /
• pp.157-172
• /
• 2004

An age-dependent branching process where disasters occur as a renewal process leading to annihilation or survival of all the cells, is considered. For such a process, the total mean sojourn time of all the cells in the system is analysed using the regeneration point technique. The mean number of cells which die in time t and its asymptotic behaviour are discussed. When the disasters arrival as a Poisson process and the lifetime of the cells follows exponential distribution, elegant inter- relationships are found among the means of (i) the total number of cells which die in time t (ii) the total sojourn time of all cells in the system upto time t and (iii) the number of living cells at time t. Some of the existing results are deduced as special cases for related processes.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.19 no.1
• /
• pp.23-45
• /
• 2015

We consider counterparty risk in CDS rates. To do so, we use a multivariate jump diffusion process for obligors' default intensity, where jumps (i.e. magnitude of contribution of primary events to default intensities) occur simultaneously and their sizes are dependent. For these simultaneous jumps and their sizes, a homogeneous Poisson process. We apply copula-dependent default intensities of multivariate Cox process to derive the joint Laplace transform that provides us with joint survival/default probability and other relevant joint probabilities. For that purpose, the piecewise deterministic Markov process (PDMP) theory developed in [7] and the martingale methodology in [6] are used. We compute survival/default probability using three copulas, which are Farlie-Gumbel-Morgenstern (FGM), Gaussian and Student-t copulas, with exponential marginal distributions. We then apply the results to calculate CDS rates assuming deterministic rate of interest and recovery rate. We also conduct sensitivity analysis for the CDS rates by changing the relevant parameters and provide their figures.

• Journal of the Korean Statistical Society
• /
• v.23 no.2
• /
• pp.483-498
• /
• 1994

Suppose that a stationary process ${X_t}$ has a marginal distribution whose support consists of sufficiently large integers. We are concerned with some analogous law of large numbers for such distribution function F. In particular, we determine a weak law of large numbers for maximum queueing length in $M/M\infty$ system. We also present a limiting behavior for the maxima based on AR(1) process with binomial thining and poisson marginals (INAR(1)) introduced by E. Mckenzie. It turns out that the result of AR(1) process is the same as that of $M/M/\infty$ queueing process in limit when we observe the queues at regularly spaced intervals of time.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.3
• /
• pp.83-91
• /
• 1999

In order to build and manage an ATM network effectively under several types of control methods, it is necessary to estimate the performance of the equipments in various viewpoints, especially of ATM multiplexer. As for the method to model the input stream into the ATM multiplexer, many researches have been done to characterize it by, such as, fluid flow, MMPP(Markov Modulated Poisson Process), or MMDP (Markov Modulated Deterministic Process). We introduce an MRP(Markov Renewal Process) to model the input stream which has proper structure to represent the burst traffic with high correlation. In this paper, we build a model for aggregated heterogeneous ON-OFF sources of ATM traffic by MRP. We make discrete time MR/D/1/B queueing system, whose input process is the superposed MRP and present a performance analysis by finding CLP(Cell Loss Probability). A simulation is done to validate our algorithm.

• Journal of Applied Reliability
• /
• v.9 no.2
• /
• pp.121-130
• /
• 2009

The power law process model the Rate of occurrence of failures(ROCOF) with monotonic trend during the operating time. However, the power law process is inappropriate when a non-monotonic trend in the failure data is observed. In this paper we deals with the reliability modeling of the failure process of large and complex repairable system whose rate of occurrence of failures shows the non-monotonic trend. We suggest a sectional model and a change-point test based on the Schwarz information criterion(SIC) to describe the non-monotonic trend. Maximum likelihood is also suggested to estimate parameters of sectional model. The suggested methods are applied to field data from an repairable system.