• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.453-462
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• 2005

We derive the influence function on t statistic and find its feature; the influence function on t statistic has two forms depending on the value of ${\mu}_0$. Sample influence functions are used to verify the validity of the derived influence function. We use random samples from normal distribution to show the validity of the function. The simulation study proves that the obtained influence function is very accurate to in estimating changes in t statistic when an observation is added or deleted.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.99-107
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• 2018

This article considers a robust Kalman filter from the M-estimation point of view. Pak (Journal of the Korean Statistical Society, 27, 507-514, 1998) proposed a particular M-estimating function which has the data-based shaping constants. The Kalman filter with the proposed M-estimating function is considered. The structure and the estimating algorithm of the Kalman filter accompanying the M-estimating function are mentioned. Kalman filter estimates by the proposed M-estimating function are shown to be well behaved even when data are contaminated.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.199-212
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• 2002

This article introduces transfer function model (TFM) with conditional heteroscedasticity where ARCH concept is built into the traditional TFM of Box and Jenkins (1976). Model building strategies such as identification, estimation and diagnostics of the model are discussed and are illustrated via empirical study including simulated data and real data as well. Comparisons with the classical TFM are also made.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• 대한산업공학회지
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• 제15권2호
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• pp.33-44
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• 1989

This paper presents stochastic-statistical dominance rules which eliminate dominated alternatives thereby reduce the number of satisficing alternatives to a manageable size so that the decision maker can choose the best alternative among them when neither the utility function nor the probability distribution of outcomes is exactly known. Specifically, it is assumed that only the characteristics of the utility function and the value function are known. Also, it is assumed that prior probabilities of the mutually exclusive states of nature are not known, but their relative bounds are known. First, the notion of relative risk aversion is used to describe the decision maker's attitude toward risk, which is defined with the acknowledgement that the utility function of the decision maker is a composite function of a cardinal value function and a utility function with-respect to the value function. Then, stochastic-statistical dominance rules are developed to screen out dominated alternatives according to the decision maker's attitude toward risk represented in the form of the measure of relative risk aversion.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.129-134
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• 2004

In this study, we investigate the effects of the weight function in the bounded influence regression quantile (BIRQ) estimator for the AR(1) model with additive outliers. In order to down-weight the outliers of X-axis, the Mallows' (1973) weight function has been commonly used in the BIRQ estimator. However, in our Monte Carlo study, the BIRQ estimator using the Tukey's bisquare weight function shows less MSE and bias than that of using the Mallows' weight function or Huber's weight function.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.135-145
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• 1995

A new algorithm was given to successively fit the multiexponential function/nonlinear function to data by a weighted least squares method, using Gauss-Newton, Marquardt, gradient and DUD methods for convergence. This study also considers the problem of linear-nonlimear weighted least squares estimation which is based upon the usual Taylor's formula process.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1007-1015
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• 2003

In this paper we introduced a new score generating function for the rank dispersion function in a general linear model. Based on the new score function, we derived the null asymptotic theory of the rank-based hypothesis testing in a linear model. In essence we showed that several rank test statistics, which are primarily focused on our new score generating function and new dispersion function, are mainly distribution free and asymptotically converges to a chi-square distribution.

• Communications for Statistical Applications and Methods
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• 제14권3호
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• pp.601-608
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• 2007

Lee, et al. (1999) proposed a unique but universal robust objective function replacing the square objective function for the radial basis function network, and demonstrated some advantages. In this article, the robust objective function in Lee, et al. (1999) is adapted for a multilayer perceptron (MLP). The shape of the robust objective function is formed by the scale parameter. Another method of determining a proper value of that parameter is proposed.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.435-446
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• 2004

In this paper we introduced a new score generating function for the rank dispersion function in a multiple linear model. Based on the new score function, we derived the asymptotic relative efficiency, ARE(11, rs), of our score function with respect to the Wilcoxon scores for the generalized F distributions which show very flexible distributions with a variety of shape and tail behaviors. We thoroughly explored the selection of r and s of our new score function that provides improvement over the Wilcoxon scores.