• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.289-298
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• 2003

We consider the estimation problem for the scale parameter of the Rayleigh distribution using weighted balanced loss function (WBLF) which reflects both goodness of fit and precision. Under WBLF, we obtain the optimal estimator which creates a kind of balance between Bayesian and non-Bayesian estimation. We also deal with the estimation of R ＝ P(Y < X) when Y and X are two independent but not identically distributed Rayleigh distribution under squared error loss function.

• Journal of Applied Reliability
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• v.1 no.1
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• pp.17-33
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• 2001

The characteristics of Weibull distribution are investigated as a function of shape parameter. The statistical estimation methods of the shape parameter and statistical comparison methods of two or more shape parameters are studied. Assuming Weibull distribution, statistical analysis of bending-strength data of alumina titanium carbide ceramic matrix composites machined two different methods are performed.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.421-441
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• 2017

In this survey, estimation methods for structural vector autoregressive models are presented in a systematic way. Both frequentist and Bayesian methods are considered. Depending on the model setup and type of restrictions, least squares estimation, instrumental variables estimation, method-of-moments estimation and generalized method-of-moments are considered. The methods are presented in a unified framework that enables a practitioner to find the most suitable estimation method for a given model setup and set of restrictions. It is emphasized that specifying the identifying restrictions such that they are linear restrictions on the structural parameters is helpful. Examples are provided to illustrate alternative model setups, types of restrictions and the most suitable corresponding estimation methods.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.285-294
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• 2005

This paper deals with the comparison of parameter estimation methods in a 3-parameter Kappa distribution which is sometimes used in flood frequency analysis. Method of moment estimation(MME), L-moment estimation(L-ME), and maximum likelihood estimation(MLE) are applied to estimate three parameters. The performance of these methods are compared by Monte-carlo simulations. Especially for computing MME and L-ME, three dimensional nonlinear equations are simplified to one dimensional equation which is calculated by the Newton-Raphson iteration under constraint. Based on the criterion of the mean squared error, L-ME (or MME) is recommended to use for small sample size( n$\le$100) while MLE is good for large sample size.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.2119-2129
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• 2020

Flow-accelerated corrosion (FAC), liquid droplet impingement erosion (LDIE), cavitation and flashing can cause continuous wall-thinning in nuclear secondary pipes. In order to prevent pipe rupture events resulting from the wall-thinning, most NPPs (nuclear power plants) implement their management programs, which include periodic thickness inspection using UT (ultrasonic test). Meanwhile, it is well known in field experiences that the thickness measurement errors (or deviations) are often comparable with the amount of thickness reduction. Because of these errors, it is difficult to estimate wall-thinning exactly whether the significant thinning has occurred in the inspected components or not. In the previous study, the authors presented an approximate estimation procedure as the first step for thickness measurement deviations at each inspected component and the statistical & quantitative characteristics of the measurement deviations using plant experience data. In this study, statistical significance was quantified for the current methods used for wall-thinning determination. Also, the authors proposed new estimation procedures for determining local wall-thinning to overcome the weakness of the current methods, in which the proposed procedure is based on analysis of variance (ANOVA) method using subgrouping of measured thinning values at all measurement grids. The new procedures were also quantified for their statistical significance. As the results, it is confirmed that the new methods have better estimation confidence than the methods having used until now.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.47-55
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• 2001

Usually estimating the means is used for statistical inference. However depending the purpose of survey, sometimes totals will give the better and more meaningful in statistical inference than the means. Here in this study, we dealt with the unemployment population of small areas with using 4 different small area estimation methods: Direct, Synthetic, Composite, Bayes estimation. For all the estimates considered in this study, the average of absolute bias and men square error were obtained in the Monte Carlo Study which was simulated using data from 1998 Economic Active Population Survey in Korea.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.411-421
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• 2023

This paper provides a new estimation equation based on the concept of a minimum distance between the empirical and theoretical distribution functions under the most widely used progressive Type-II censoring scheme. For illustrative purposes, simulated and real datasets from a three-parameter Weibull distribution are analyzed. For comparison, the most popular estimation methods, the maximum likelihood and maximum product of spacings estimation methods, are developed together. In the analysis of simulated datasets, the excellence of the provided estimation method is demonstrated through the degree of the estimation failure of the likelihood-based method, and its validity is demonstrated through the mean squared errors and biases of the estimators obtained from the provided estimation equation. In the analysis of the real dataset, two types of goodness-of-fit tests are performed on whether the observed dataset has the three-parameter Weibull distribution under the progressive Type-II censoring scheme, through which the performance of the new estimation equation provided is examined.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.337-347
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• 2024

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.519-533
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• 2004

In this paper, we suggest quasi-likelihood estimation (QLE) method and its robust version in estimating spatial dependence modelled through variogram used for spatial data modelling. We compare the statistical characteristics of the estimators with other popular least squares estimators of parameters for variogram model by simulation study. The QLE method for estimating spatial dependence has the advantages that it does not need the concept of lags commonly required for least squares estimation methods as well as its statistical superiority. The QLE method also shows the statistical superiority to the other methods for the tested Gaussian and non-Gaussian spatial processes.