Yazar "Kinaci, Ismail" seçeneğine göre listele

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    ESTIMATION OF P(Y < X) FOR DISTRIBUTIONS HAVING POWER HAZARD FUNCTION
    (ISOSS PUBL, 2014) Kinaci, Ismail
    In this paper, the stress-strength reliability is obtained when the underlying distribution has power hazard function. Maximum likelihood and uniformly minimum variance unbiased estimate of the stress-strength reliability are derived and various distributional properties of these estimates are discussed. Exact and asymptotic confidence intervals for the stress-strength reliability are constructed. Simulation study is also performed to investigate the coverage probabilities of these intervals.
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    ON THE NUMBER OF l--OVERLAPPING SUCCESS RUNS OF LENGTH k UNDER q-- SEQUENCE OFBINARY TRIALS
    (2016) Kinaci, Ismail; Kuş, Coşkun; Karakaya, Kadir; Akdoğan, Yunus
    Let X1,X2,...,X.. be {0,1}-valued Bernoulli trials with geometrically varying success probability. The probability mass function, first and second moments of the number of l-overlapping success runs of length k: in X1,X2,...,X,, are obtained. The new distribution generalizes, Type and Type II q-binomial distributions which were recently studied in the literature.
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    Optimal experimental plan for multi-level stress testing with Weibull regression under progressive Type-II extremal censoring
    (TAYLOR & FRANCIS INC, 2017) Ng, Hon Keung Tony; Kinaci, Ismail; Kus, Coskun; Chan, Ping Shing
    In the design of constant-stress life-testing experiments, the optimal allocation in a multi-level stress test with Type-I or Type-II censoring based on the Weibull regression model has been studied in the literature. Conventional Type-I and Type-II censoring schemes restrict our ability to observe extreme failures in the experiment and these extreme failures are important in the estimation of upper quantiles and understanding of the tail behaviors of the lifetime distribution. For this reason, we propose the use of progressive extremal censoring at each stress level, whereas the conventional Type-II censoring is a special case. The proposed experimental scheme allows some extreme failures to be observed. The maximum likelihood estimators of the model parameters, the Fisher information, and asymptotic variance-covariance matrices of the maximum likelihood estimates are derived. We consider the optimal experimental planning problem by looking at four different optimality criteria. To avoid the computational burden in searching for the optimal allocation, a simple search procedure is suggested. Optimal allocation of units for two- and four-stress-level situations is determined numerically. The asymptotic Fisher information matrix and the asymptotic optimal allocation problem are also studied and the results are compared with optimal allocations with specified sample sizes. Finally, conclusions and some practical recommendations are provided.
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    Statistical Inference for Weibull Distribution Based on Competing Risk Data under Progressive Type-I Group Censoring
    (Selçuk Üniversitesi, 2015) Unal, Esra; Wu, Shou Jye; Bekci, Muhammet; Kinaci, Ismail; Kus, Coskun
    In this study, statistical inference is discussed for Weibull distribution based on competing risks data under progressive Type-I group censoring. The maximum likelihood procedure is used to get point estimates and asymptotic confidence intervals for unknown parameters. Some simulations results are presented. A numerical example is also provided.
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    Statistical inference of stress-strength reliability for the exponential power (EP) distribution based on progressive type-II censored samples
    (HACETTEPE UNIV, FAC SCI, 2017) Akdam, Neriman; Kinaci, Ismail; Saracoglu, Bugra
    Suppose that X represents the stress which is applied to a component and Y is strength of this component. Let X and Y have Exponential Power (EP) distribution with (alpha(1), beta(1)) and (alpha(2), beta(2)) parameters, respectively. In this case, stress-strength reliability (SSR) is shown by P = P (X < Y). In this study, the SSR for EP distribution are obtained with numerical methods. Also maximum likelihood estimate (MLE) and approximate bayes estimates by using Lindley approximation method under squared-error loss function for SSR under progressive type-II censoring are obtained. Moreover, performances of these estimators are compared in terms of MSEs by using Monte Carlo simulation. Furthermore coverage probabilities of parametric bootstrap estimates are computed. Finally, real data analysis is presented.