10 results
Search Results
Now showing 1 - 10 of 10
Article Citation - WoS: 12Citation - Scopus: 14The Markov Discrete Time Δ-Shock Reliability Model and a Waiting Time Problem(Wiley, 2022) Chadjiconstantinidis, Stathis; Eryilmaz, Serkandelta-shock model is one of the widely studied shock models in reliability theory and applied probability. In this model, the system fails due to the arrivals of two consecutive shocks which are too close to each other. That is, the system breaks down when the time between two successive shocks falls below a fixed threshold delta. In the literature, the delta-shock model has been mostly studied by assuming that the time between shocks have continuous distribution. In the present paper, the discrete time version of the model is considered. In particular, a proper waiting time random variable is defined based on a sequence of two-state Markov dependent binary trials and the problem of finding the distribution of the system's lifetime is linked with the distribution of the waiting time random variable, and we study the joint as well as the marginal distributions of the lifetime, the number of shocks and the number of failures associated with these binary trials.Article Citation - WoS: 30Citation - Scopus: 34Discrete Time Shock Models in a Markovian Environment(Ieee-inst Electrical Electronics Engineers inc, 2016) Eryilmaz, SerkanThis paper deals with two different shock models in a Markovian environment. We study a system from a reliability point of view under these two shock models. According to the first model, the system fails if the cumulative shock magnitude exceeds a critical level, while in the second model the failure occurs when the cumulative effect of the shocks in consecutive periods is above a critical level. The shock occurrences over discrete time periods are assumed to be Markovian. We obtain expressions for the failure time distributions of the system under the two model. Illustrative computational results are presented for the survival probabilities and mean time to failure values of the system.Article Citation - WoS: 13Citation - Scopus: 14On Mean Residual Life of Discrete Time Multi-State Systems(Nctu-national Chiao Tung Univ Press, 2013) Eryilmaz, SerkanThe mean residual life function is an important characteristic in reliability and survival analysis. Although many papers have studied the mean residual life of binary systems, the study of this characteristic for multi-state systems is new. In this paper, we study mean residual life of discrete time multi-state systems that have M + 1 states of working efficiency. In particular, we consider two different definitions of mean residual life function and evaluate them assuming that the degradation in multi-state system follows a Markov process.Article Citation - WoS: 4Citation - Scopus: 6Statistical Inference for a Class of Startup Demonstration Tests(Taylor & Francis inc, 2019) Eryilmaz, SerkanIn this article, we develop a general statistical inference procedure for the probability of successful startup p in the case of startup demonstration tests when only the number of trials until termination of the experiment are observed. In particular, we define a class of startup demonstration tests and present expectation-maximization (EM) algorithm to get the maximum likelihood estimate of p for this class. Most of well-known startup testing procedures are involved in this class. Extension of the results to Markovian startups is also presented.Article Citation - WoS: 9Start-Up Demonstration Tests Under Markov Dependence(Isoss Publ, 2010) Eryilmaz, SerkanA start-up demonstration test is a mechanism that can be used to demonstrate the reliability of an equipment to the customer. In this paper, we study CSTF (consecutive successes total failures) and TSTF (total successes total failures) procedures assuming that the individual start-ups follow a first order Markov dependence structure. Explicit (nonrecursive) expressions for the distributions of the test lengths are provided. Maximum likelihood and moments estimators of the expected test length are obtained and some numerical results are presented for an illustration.Article Citation - WoS: 11Citation - Scopus: 12Discrete Time Shock Models Involving Runs(Elsevier Science Bv, 2015) Eryilmaz, SerkanIn this paper, three different discrete time shock models are studied. In the first model, the failure occurs when the additively accumulated damage exceeds a certain level while in the second model the system fails upon the local damage caused by the consecutively occurring shocks. The third model is a mixed model and combines the first and second models. The survival functions of the systems under these models are obtained when the occurrences of the shocks are independent, and when they are Markov dependent over the periods. (C) 2015 Elsevier B.V. All rights reserved.Article Citation - WoS: 6Citation - Scopus: 7DISTRIBUTIONS OF RANDOM VARIABLES INVOLVED IN DISCRETE CENSORED δ-SHOCK MODELS(Cambridge Univ Press, 2023) Chadjiconstantinidis, Stathis; Eryilmaz, SerkanSuppose that a system is affected by a sequence of random shocks that occur over certain time periods. In this paper we study the discrete censored delta-shock model, delta <= 1 , for which the system fails whenever no shock occurs within a -length time period from the last shock, by supposing that the interarrival times between consecutive shocks are described by a first-order Markov chain (as well as under the binomial shock process, i.e., when the interarrival times between successive shocks have a geometric distribution). Using the Markov chain embedding technique introduced by Chadjiconstantinidis et al. (Adv. Appl. Prob. 32, 2000), we study the joint and marginal distributions of the system's lifetime, the number of shocks, and the number of periods in which no shocks occur, up to the failure of the system. The joint and marginal probability generating functions of these random variables are obtained, and several recursions and exact formulae are given for the evaluation of their probability mass functions and moments. It is shown that the system's lifetime follows a Markov geometric distribution of order (a geometric distribution of order under the binomial setup) and also that it follows a matrix-geometric distribution. Some reliability properties are also given under the binomial shock process, by showing that a shift of the system's lifetime random variable follows a compound geometric distribution. Finally, we introduce a new mixed discrete censored delta -shock model, for which the system fails when no shock occurs within a -length time period from the last shock, or the magnitude of the shock is larger than a given critical threshold . gamma > 0. Similarly, for this mixed model, we study the joint and marginal distributions of the system's lifetime, the number of shocks, and the number of periods in which no shocks occur, up to the failure of the system, under the binomial shock process.Article Citation - WoS: 14Citation - Scopus: 14A Regime Switching Model for Temperature Modeling and Applications To Weather Derivatives Pricing(Springer Heidelberg, 2020) Turkvatan, Aysun; Omay, Tolga; Hayfavi, Azize; Omay, Tolga; Omay, Tolga; Economics; EconomicsIn this study, we propose a regime-switching model for temperature dynamics, where the parameters depend on a Markov chain. We improve upon the traditional models by modeling jumps in temperature dynamics via the chain itself. Moreover, we compare the performance of the proposed model with the existing models. The results indicate that the proposed model outperforms in the short time forecast horizon while the forecast performance of the proposed model is in line with the existing models for the long time horizon. It is shown that the proposed model is a relatively better representation of temperature dynamics compared to the existing models. Furthermore, we derive prices of weather derivatives written on several temperature indices.Article Citation - WoS: 5Citation - Scopus: 6On the Mean and Extreme Distances Between Failures in Markovian Binary Sequences(Elsevier Science Bv, 2011) Eryilmaz, Serkan; Yalcin, FeminThis paper is concerned with the mean, minimum and maximum distances between two successive failures in a binary sequence consisting of Markov dependent elements. These random variables are potentially useful for the analysis of the frequency of critical events occurring in certain stochastic processes. Exact distributions of these random variables are derived via combinatorial techniques and illustrative numerical results are presented. (C) 2011 Elsevier B.V. All rights reserved.Article Citation - Scopus: 11Start-up demonstration tests under Markov dependence(2010) Eryilmaz,S.A start-up demonstration test is a mechanism that can be used to demonstrate the reliability of an equipment to the customer. In this paper, we study CSTF (consecutive successes total failures) and TSTF (total successes total failures) procedures assuming that the individual start-ups follow a first order Markov dependence structure. Explicit (nonrecursive) expressions for the distributions of the test lengths are provided. Maximum likelihood and moments estimators of the expected test length are obtained and some numerical results are presented for an illustration. © 2010 Pakistan Journal of Statistics.
