• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.543-556
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• 2015

We present a survey of contributions that defined the nature and extent of robust statistics for the last 50 years. From the pioneering work of Tukey, Huber, and Hampel that focused on robust location parameter estimation, we presented various generalizations of these estimation procedures that cover a wide variety of models and data analysis methods. Among these extensions, we present linear models, clustered and dependent observations, times series data, binary and discrete data, models for spatial data, nonparametric methods, and forward search methods for outliers. We also present the current interest in robust statistics and conclude with suggestions on the possible future direction of this area for statistical science.

• Computational Structural Engineering : An International Journal
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• 제2권1호
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• pp.1-10
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• 2002

Information on the distribution of the basic random variables is essential for the accurate evaluation of structural reliability. The usual method for determining the distribution is to fit a candidate distribution to the histogram of available statistical data of the variable and perform appropriate goodness-of-fit tests. Generally, such candidate distributions would have two parameters that may be evaluated from the mean value and standard deviation of the statistical data. In the present paper, a-parameter Gamma distribution, whose parameters can be directly defined in terms of the mean value, standard deviation and skewness of available data, is suggested. The flexibility and advantages of the distribution in fitting statistical data and its significance in structural reliability evaluation are identified and discussed. Numerical examples are presented to demonstrate these advantages.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.213-228
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• 2008

Using a linear loss function, this paper considers the one-sided testing problem for the exponential distribution via the empirical Bayes(EB) approach. Based on right censored data, we propose an EB test for the exponential parameter and obtain its convergence rate and asymptotic optimality, firstly, under the condition that the censoring distribution is known and secondly, that it is unknown.

• Computational Structural Engineering : An International Journal
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• 제1권2호
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• pp.81-87
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• 2001

A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.71-80
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• 2007

The present paper investigates estimation of a scale parameter of a gamma distribution using a loss function that reflects both goodness of fit and precision of estimation. The Bayes and empirical Bayes estimators rotative to balanced loss functions (BLFs) are derived and optimality of some estimators are studied.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.291-303
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• 2003

This work is proposed an alternative identification method of outlying cell which is one of important issues in categorical data analysis. One finds that there is a strong relationship between the location of an outlying cell and the corresponding parameter estimates of the well-fitted log-linear model. Among parameters of log-linear model, an outlying cell is affected by interaction terms rather than main effect terms. Hence one could identify an outlying cell by investigating of parameter estimates in an appropriate log-linear model.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.353-359
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• 2003

This paper deals with the problem of testing mean change with one change-point with the normal random variables. We propose a test with the mean difference for change in a location parameter. A power comparison study of various change-point test statistics is performed via Monte Carlo simulation with S-plus software.

• Journal of the Korean Statistical Society
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• 제32권2호
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• pp.151-162
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• 2003

In this paper, we propose two selection procedures for selecting populations better than a control population. The bestness is defined in terms of location parameter. One of the procedures is based on two-sample linear rank statistics whereas the other one is based on a comparatively simple statistic, and is useful when testing time is expensive so that an early termination of an experiment is desirable. The proposed selection procedures are seen to be strongly monotone. Performance of the proposed procedures is assessed through simulation study.

• Communications for Statistical Applications and Methods
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• 제23권5호
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• pp.411-421
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• 2016

Large frequency limiting distributions of two errors in realized covariance are investigated under noisy and non-synchronous high frequency sampling situations. The first distribution characterizes increased variance of the realized covariance due to noise for large frequency and the second distribution characterizes decreased variance of the realized covariance due to discretization for large frequency. The distribution of the combined error enables us to determine the sampling frequency which depends on a nuisance parameter. A consistent estimator of the nuisance parameter is proposed.

• Journal of the Korean Statistical Society
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• 제16권2호
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• pp.102-112
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• 1987

This paper treats the problem of estimating an arbitrary parametric function in the case when the parameter and sample spaces are countable and the decision space is arbitrary. Using the notions of a stepwise Bayesian procedure and finite admissibility, a theorem is proved. It shows that under some assumptions, every finitely admissible estimator is unique stepwise Bayes with respect to a finite or countable sequence of mutually orthogonal priors with finite supports. Under an additional assumption, it is shown that the converse is true as well. The first can be also extended to the case when the parameter and sample space are arbitrary, i.e., not necessarily countable, and the underlying probability distributions are discrete.