• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.99-106
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• 2010

In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.213-220
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• 1995

We consider a class of the weighted logrank tests and 4 types of weights in this class. We propese a test based on the maximum of 4 weighted logrank statistics and suggest a simulation techniqur to obtain the p-value of proposed test. It is shown through the simulation studies that the proposed test is robust and has reasonably good powers comparing with the well known efficient tests.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.287-299
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• 2007

We consider a multiclass queue where queued customers are served in their order of arrival at a rate which depends on the customer type. By using the asymptotic results obtained by Dabrowski et al. (2006) we calculate the sharp asymptotics of the stationary distribution of the number of customers of each class in the system and the distribution of the number of customers of each class when the total number of customers reaches a high level before emptying. We also obtain a fast simulation algorithm to estimate the overflow probability and compare it with the general simulation and asymptotic results.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.117-128
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• 2012

In Baccarat, the gambler can bet on either the Player or Banker. The only gambler's strategy is to consider the previous winning history on the round. The winning probabilities of Player or Banker are calculated by simulation using R. Conditional winning probabilities given that Player or Banker has won i consecutive times are also calculated by simulation. Conditional winning probability implies that the sequence of Baccarat results is an almost independent sequence of events. It has been shown that the total amount of returns in each round of games is almost identical to a random walk. Thus, one possible strategy is to catch the trend(the Player or the Banker) of the random walk and to bet on that side of the trend.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.97-108
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• 2020

This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.

• KCI Concrete Journal
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• v.12 no.2
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• pp.11-21
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• 2000

It is not easy to correctly predict deflections of reinforced concrete beams and one-way slabs due to the variability of parameters involved in the calculation of deflections. Monte Carlo simulation is used to assess the variability of deflections with known statistical data and probability distributions of variables. A deterministic deflection value is obtained using the layered beam model based on the finite element approach in which a finite element is divided into a number of layers over the depth. The model takes into account nonlinear effects such as cracking, creep and shrinkage. Statistical parameters were obtained from the literature. For the assessment of variability of deflections, 12 cases of one-way slabs and T-beams are designed on the basis of ultimate moment capacity. Several results of a probabilistic study are presented to indicate general trends indicated by results and demonstrate the effect of certain design parameters on the variability of deflections. From simulation results, the variability of deflections relies primarily on the ratio of applied moment to cracking moment and the corre-sponding reinforcement ratio.

• ETRI Journal
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• v.24 no.3
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• pp.197-204
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• 2002

This paper presents the results of the numerical simulation of a multibeam active phased array antenna for a High Altitude Platform Station (HAPS). The simulation takes into account the random errors caused by the non-identity of the array elements and the inaccuracy of the antenna calibration. The results of our statistical simulation show that the strict requirements on the sidelobe envelope for HAPSs can be met when the amplitude and phase distribution errors are minor, a condition which may be achieved by using digital beamforming.

• Current Optics and Photonics
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• v.1 no.5
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• pp.551-555
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• 2017

Tissue optics is a well-established and extensively studied area. In the last decades, Monte Carlo simulation (MCS) has been one of the standard tools for simulation of light propagation in turbid media. The utilization of parallel processing exhibits dramatic increase in the speed of MCS's of photon migration. Some calculations based on MCS can be completed within a few seconds. Since the MCS's have the potential to become a real time calculation method, a dynamic accuracy estimation, which is also known as history by history statistical estimators, is required in the simulation code to automatically terminate the MCS as the results' accuracy achieves a high enough level. In this work, spatial and time-domain GPU-based MCS, adopting the dynamic accuracy estimation, are performed to calculate the light dose/reflectance in homogeneous and heterogeneous tissue media. This dynamic accuracy estimation can effectively derive the statistical error of optical dose/reflectance during the parallel Monte Carlo process.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.65-85
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• 2024

The tail probability of a function of a multivariate random variable is not easy to estimate by the crude Monte Carlo simulation. When the occurrence of the function value over a threshold is rare, the accurate estimation of the corresponding probability requires a huge number of samples. When the explicit form of the cumulative distribution function of each component of the variable is known, the inverse transform likelihood ratio method is directly applicable scheme to estimate the tail probability efficiently. The method is a type of the importance sampling and its efficiency depends on the selection of the importance sampling distribution. When the cumulative distribution of the multivariate random variable is represented by a copula and its marginal distributions, we develop an iterative algorithm to find the optimal importance sampling distribution, and show the convergence of the algorithm. The performance of the proposed scheme is compared with the crude Monte Carlo simulation numerically.