• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.355-371
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• 2018

The two parameter negative exponential distribution has many practical applications in queuing theory such as the service times of agents in system, the time it takes before your next telephone call, the time until a radioactive practical decays, the distance between mutations on a DNA strand, and the extreme values of annual snowfall or rainfall; consequently, has many applications in reliability systems. This paper considers an estimation problem of stress-strength model with two parameter negative parameter exponential distribution. We introduce a maximum penalized likelihood method, Bayes estimator using Lindley approximation to estimate stress-strength model and compare the proposed estimators with regular maximum likelihood estimator for complete data. We also introduce a maximum penalized likelihood method, Bayes estimator using a Markov chain Mote Carlo technique for incomplete data. A Monte Carlo simulation study is performed to compare stress-strength model estimates. Real data is used as a practical application of the proposed model.

• Kyungpook Mathematical Journal
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• 제52권2호
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• pp.167-177
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• 2012

In this article, we propose exponentially fitted error correction methods(EECM) which originate from the error correction methods recently developed by the authors (see [10, 11] for examples) for solving nonlinear stiff initial value problems. We reduce the computational cost of the error correction method by making a local approximation of exponential type. This exponential local approximation yields an EECM that is exponentially fitted, A-stable and L-stable, independent of the approximation scheme for the error correction. In particular, the classical explicit Runge-Kutta method for the error correction not only saves the computational cost that the error correction method requires but also gives the same convergence order as the error correction method does. Numerical evidence is provided to support the theoretical results.

• 응용통계연구
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• 제22권5호
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• pp.937-942
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• 2009

본 논문에서는 보험 상품의 잉여금(surplus)을 확률적으로 모형화한 후, 잉여금의 파산 확률과 이의 근사 공식들을 소개한다. 잉여금은 일정한 율(rate)로 들어오는 프리미엄(premium)에 의해 증가한다. 보험금 청구(claim)는 포아송 과정(Poisson process)을 따라 발생하고 보험금 청구가 있을 때마다 잉여금은 임의의 양(random amount) 만큼 줄어든다. 잉여금이 0이하로 떨어지면 파산(ruin)이 발생한다고 한다. 이와 같은 리스크(risk) 모형에서 파산 확률의 이론적 공식은 잘 알려져 있으나, 공식에 n차 공률(convolution)과 무한 합(infinite sum)이 포함되어 있어 실질적인 계산은 불가능하다. 본 논문에서는 잘 알려진 De Vylder의 근사 공식과 지수적인 근사 공식(exponential approximation)을 소개하고, 이들을 일반화한 새로운 근사 공식을 제안한다. 기존 근사 공식과의 수치적 비교를 통해 새로 제안된 근사 공식의 우월성을 보인다.

• Journal of the Korean Statistical Society
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• 제19권2호
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• pp.160-170
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• 1990

A new approximation method is proposed for the ARL of CUSUM chart which is based on the log probability ratio statistic. This method uses the condition of before-stopping time to derive the expectation of excess over boundaries. The proposed method is compared to some other approximation methods in normal and exponential cases.

• Journal of the Korean Data and Information Science Society
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• 제22권6호
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• pp.1217-1222
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• 2011

In this paper, we show that any circular density can be closely approximated by an exponential family of distributions. Therefore we propose an exponential family of distributions as a new family of circular distributions, which is absolutely suitable to model any shape of circular distributions. In this family of circular distributions, the trigonometric moments are found to be the uniformly minimum variance unbiased estimators (UMVUEs) of the parameters of distribution. Simulation result and goodness of fit test using an asymmetric real data set show usefulness of the novel circular distribution.

• Nonlinear Functional Analysis and Applications
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• 제26권1호
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• pp.35-64
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• 2021

In this paper, we investigate an initial boundary value problem for a nonlinear pseudoparabolic equation. At first, by applying the Faedo-Galerkin, we prove local existence and uniqueness results. Next, by constructing Lyapunov functional, we establish a sufficient condition to obtain the global existence and exponential decay of weak solutions.

• Geomechanics and Engineering
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• 제13권5호
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• pp.845-860
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• 2017

It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

• 디지털산업정보학회논문지
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• 제9권3호
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• pp.21-30
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• 2013

Powell-Miller theory is a good method to express or treat incorrect information. But it has limitation that requires too much time to apply to actual situation because computational complexity increases in exponential and functional way. Accordingly, there have been several attempts to reduce computational complexity but side effect followed - certainty factor fell. This study suggested expanded Approximation Algorithm. Expanded Approximation Algorithm is a method to consider both smallest supersets and largest subsets to expand basic space into a space including inverse set and to reduce Approximation error. By using expanded Approximation Algorithm suggested in the study, basic probability assignment function value of subsets was alloted and added to basic probability assignment function value of sets related to the subsets. This made subsets newly created become Approximation more efficiently. As a result, it could be known that certain function value which is based on basic probability assignment function is closely near actual optimal result. And certainty in correctness can be obtained while computational complexity could be reduced. by using Algorithm suggested in the study, exact information necessary for a system can be obtained.

• 대한기계학회논문집A
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• 제27권6호
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• pp.1041-1049
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• 2003

In this paper, a new local two-point approximation method which is based on the exponential intervening variable is proposed. This new algorithm, called the Two-Point Convex Approximation(TPCA), use the function and design sensitivity information from the current and previous design points of the sequential approximate optimization to generate a sequence of convex, separable subproblems. This paper describes the derivation of the parameters associated with the approximation and the numerical solution procedure. In order to show the numerical performance of the proposed method, a sequential approximate optimizer is developed and applied to solve several typical design problems. These optimization results are compared with those of other optimizers. Numerical results obtained from the test examples demonstrate the effectiveness of the proposed method.

• 산업공학
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• 제11권3호
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• pp.55-63
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• 1998

In this study we consider a CONWIP system in which the processing times at each station follow an exponential distribution and the demands for the finished products arrive according to a compound Poisson process. The demands that are not satisfied instantaneously are assumed to be lost. We assume that the lot size at each station is greater than one. For this system we develop an approximation method to obtain the performance measures such as steady state probabilities of the number of parts at each station, average number of parts at each station and the proportion of lost demands. For the analysis of the proposed CONWIP system, we model the CONWIP system as a closed queueing network with a synchronization station and analyze the closed queueing network using a product form approximation method. A recursive technique is used to solve the subnetwork in the application of the product-form approximation method. To test the accuracy of the approximation method, the results obtained from the approximation method were compared with those obtained by simulation. Comparisons with simulation have shown that the accuracy of the approximate method is acceptable.