• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.351-368
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• 2021

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ². The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ² is small and exhibits a meaningful decrease in the RMSE under moderate and large σ² values.

• 한국공작기계학회논문집
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• 제18권1호
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.1-10
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• 2002

A Bayes estimator of the scale parameter in an exponential distribution will be considered by a LINEX error, then the risk of the Bayes estimator using a LINEX loss will be compared with that of a Bayes estimator using a square error.

• 응용통계연구
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• 제6권1호
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• pp.79-93
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• 1993

회귀모형에 있어서의 Ruppert와 Carroll의 절사 회귀 추정법을 확장하여 회귀 분위수에 의 한 두 개의 두분으로 관측치를 분할하여 각 부분마다 가중치를 달리 부여하는 방법으로 적 합된 L-추정법을 제안하였다. 이 제안된 L-추정법은 특히 비대칭인 오차분포하에서 좋은 효율을 가지고 있었다.

• Structural Engineering and Mechanics
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• 제8권5호
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• pp.513-529
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• 1999

A concept of hierarchical modeling, the newest modeling technology, has been introduced in early 1990's. This new technology has a great potential to advance the capabilities of current computational mechanics. A first step to implement this concept is to construct hierarchical models, a family of mathematical models sequentially connected by a key parameter of the problem under consideration and have different levels in modeling accuracy, and to investigate characteristics in their numerical simulation aspects. Among representative model problems to explore this concept are elastic structures such as beam-, arch-, plate- and shell-like structures because the mechanical behavior through the thickness can be approximated with sequential accuracy by varying the order of thickness polynomials in the displacement or stress fields. But, in the numerical, analysis of hierarchical models, two kinds of errors prevail, the modeling error and the numerical approximation error. To ensure numerical simulation quality, an accurate estimation of these two errors is definitely essential. Here, a local a posteriori error estimator for elastic structures with thin domain such as plate- and shell-like structures is derived using the element residuals and the flux balancing technique. This method guarantees upper bounds for the global error, and also provides accurate local error indicators for two types of errors, in the energy norm. Compared to the classical error estimators using the flux averaging technique, this shows considerably reliable and accurate effectivity indices. To illustrate the theoretical results and to verify the validity of the proposed error estimator, representative numerical examples are provided.

• Communications for Statistical Applications and Methods
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• 제19권5호
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• pp.663-671
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• 2012

In the present study, a combined ratio cum regression estimator is proposed to estimate the population mean of the study variable in the presence of a non-response using an auxiliary variable under double sampling. The expressions of bias and mean squared error(MSE) based on the proposed estimator is derived under double (or two stage) sampling to the first degree of approximation. Some estimators are also derived from the proposed class by allocating the suitable values of constants used. A comparison of the proposed estimator with the usual unbiased estimator and other derived estimators is carried out. An empirical study is carried out to demonstrate the performance of the suggested estimator and of others; it is endow that the empirical results backing the theoretical study.

• Journal of Theoretical and Applied Mechanics
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• 제3권1호
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• pp.16-33
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• 2002

A concept of hierarchical modeling, the newest modeling technology. has been introduced early In 1990. This nu technology has a goat potential to advance the capabilities of current computational mechanics. A first step to Implement this concept is to construct hierarchical models, a family of mathematical models which are sequentially connected by a key parameter of the problem under consideration and have different levels in modeling accuracy, and to investigate characteristics In their numerical simulation aspects. Among representative model problems to explore this concept are elastic structures such as beam-, arch-. plate- and shell-like structures because the mechanical behavior through the thickness can be approximated with sequential accuracy by varying the order of thickness polynomials in the displacement or stress fields. But, in the numerical analysis of hierarchical models, two kinds of errors prevail: the modeling error and the numerical approximation errors. To ensure numerical simulation quality, an accurate estimation of these two errors Is definitely essential. Here, a local a posteriori error estimator for elastic structures with thin domain such as plate- and shell-like structures Is derived using element residuals and flux balancing technique. This method guarantees upper bounds for the global error, and also provides accurate local error Indicators for two types of errors, in the energy norm. Comparing to the classical error estimators using flux averaging technique, this shows considerably reliable and accurate effectivity indices. To illustrate the theoretical results and to verify the validity of the proposed error estimator, representative numerical examples are provided.

• Journal of the Korean Statistical Society
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• 제8권1호
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• pp.23-36
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• 1979

In the multiple linear regression model a class of weighted least absolute error estimaters, which minimize the sum of weighted absolute residuals, is proposed. It is shown that the weighted least absolute error estimators with Wilcoxon scores are equivalent to the Koul's Wilcoxon type estimator. Therefore, the asymptotic efficiency of the proposed estimator with Wilcoxon scores relative to the least squares estimator is the same as the Pitman efficiency of the Wilcoxon test relative to the Student's t-test. To find the estimates the iterative weighted least squares method suggested by Schlossmacher is applicable.

• Journal of the Korean Statistical Society
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• 제34권4호
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• pp.345-366
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• 2005

This paper considers the cointegrating vector estimator in the error correction model with stationary covariates, which combines the stationary vector autoregressive model and the nonstationary error correction model. The cointegrating vector estimator is shown to follow the locally asymptotically mixed normal distribution. The variance of the estimator depends on the co­variate effect of stationary regressors, and the asymptotic efficiency improves as the magnitude of the covariate effect increases. An economic application of the money demand equation is provided.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.265-276
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• 1996

The nonparametric deconvolution problems are studied to recover an unknown density when the data are contaminated with Gaussian error. We propose the estimator which is a linear combination of kernel type estimates of derivertives of the observed density function. We show that this estimator is consistent and also consider the properties of estimator at small sample by simulation.