• Proceedings of the Korea Society for Simulation Conference
• /
• 1992.10a
• /
• pp.8-8
• /
• 1992

Through a simulation experiment, often an experimenter is concerned with estimating the system parameters of the linear model consisting of m design points from the outputs oft the simulation model. To improve the estimation of the system parameters and reliability of these estimators, appropriate simulation techniques have been developed. For the first order linear model, Schruben and Margolin (1978) exploited the random number assignment rules which uses a combination of common random numbers and antithetic streams in a simulation experiment designed to estimate the system parameters when the design matrix of simulation model admits orthogonal blocking into two blocks. Nozari, Arnold and Pegden (1984) developed a method for appliying the method of control variates to the situation of the linear model having multiple design points. This talk deals with a different way of utilizing controls under the correlation induction strategy of Schruben and Margolin's to improve the simulation efficiency, and presents a procedure for obtaining the estimators of the system parameters analytically. Simulation results on a selected simulation model indicate a promising evidence that a proposed method may yield better results than Schruben and Margolin's method.

• Communications for Statistical Applications and Methods
• /
• v.30 no.6
• /
• pp.531-550
• /
• 2023

The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.4
• /
• pp.1045-1056
• /
• 2007

We consider M/M/1 feedback queues with multi-class customers. We assume that different classes of customers have different arrival rates, service rates and feedback probabilities. Using the h-transforms of McDonald(999) we derive an importance sampling estimator for an overflow probability that the total number of customers in the system reaches a high level before emptying.

• Journal of the Korean Statistical Society
• /
• v.26 no.2
• /
• pp.155-170
• /
• 1997

We propose an integer-valued MA(q) process with Poisson disturbance. Its various properties are discussed such as the joint distribution, time reversibility and regression. We derive the asymptotic distribution of autocovariance function and estimators of the parameters in the suggested model. We also consider the relationship between INMA(q) and M/D/.infty. processes.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.11
• /
• pp.4469-4482
• /
• 2015

In this paper, we investigate the outage performance of a two-way decode-and-forward relaying network in an interference-limited Nakagami-m fading environment. More specifically, assuming the presence of Nakagami-m faded multiple co-channel interferers at the source/destination terminals, the closed-form approximate expression for the outage probability is derived by using moment-based estimators attaining the appropriate Nakagami-m fading parameter. Simulation results demonstrate that our analytical result is in excellent agreement with the Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.4
• /
• pp.749-756
• /
• 2012

An approach widely used for small area estimation is based on linear mixed models. However, when the functional form of the relationship between the response and the input variables is not linear, it may lead to biased estimators of the small area parameters. In this paper we propose M-quantile kernel regression for small area mean estimation allowing nonlinearities in the relationship between the response and the input variables. Numerical studies are presented that show the sample properties of the proposed estimation method.

• The Journal of Asian Finance, Economics and Business
• /
• v.5 no.3
• /
• pp.19-29
• /
• 2018

This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

• The Bulletin of The Korean Astronomical Society
• /
• v.37 no.1
• /
• pp.35.1-35.1
• /
• 2012

Determining the virial factor of the broad-line region (BLR) gas is crucial in calibrating AGN black hole mass estimators, since the measured line-of-sight velocity needs to be converted into the representative velocity of the BLR gas. The unknown virial factor has been empirically calibrated based on the $M_{BH}-{\sigma}_*$ relation of non-AGN galaxies, but the claimed values are different by a factor of 2 in recent studies. We investigate the origin of the difference by measuring the $M_{BH}-{\sigma}_*$ relation using the most updated nearby galaxy sample, and explore the dependence of the virial factor on the various fitting methods. We find that the discrepancy is mostly caused by the sample bias while the difference stemming from various regression methods is marginal. Based on the best-determined virial factor, we present the updated $M_{BH}-{\sigma}_*$ relation of local active galaxies.

• International Journal of Reliability and Applications
• /
• v.6 no.2
• /
• pp.117-133
• /
• 2005

Estimations of the parameters included in a two-component system are derived based on masked system life test data, when the probability of masking depends upon the exact cause of system failure. Also estimations of reliability for the individual components at a specified mission time are derived. Maximum likelihood and Bayes methods are used to derive these estimators. The problem is explained on a series system consisting of two independent components each of which has a Pareto distributed lifetime. Further we present numerical studies using simulation.

• The Korean Journal of Applied Statistics
• /
• v.6 no.1
• /
• pp.79-93
• /
• 1993

We consider adaptive L-estimation of estimating slope parameter in regression model. The proposed estimator is simple extension of trimmed least squares estimator proposed by ruppert and carroll. The efficiency of the proposed estimator is especially well compared with usual least squares estimator, least absolute value estimator, and M-estimators designed for asymmetric distributions under asymmetric error distributions.