• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.889-904
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• 2021

This study is a follow-up study of Kang and Kim (2020). In this study, we derive the sample influence functions of the t-statistic which were not directly derived in previous researches. Throughout these results, we both mathematically examine the relationship between the empirical influence function and the sample influence function, and consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between an approximated sample influence function and the empirical influence function is verified by a simulation of a random sample of size 300 from normal distribution. As a result of the simulation, the relationship between the sample influence function which is derived from the t-statistic and the empirical influence function, and the method of approximating the sample influence function through the empirical influence function were verified. This research has significance in proposing both a method which reduces errors in approximation of the empirical influence function and an effective and practical method that evolves from previous research which approximates the sample influence function directly through the empirical influence function by constant revision.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.563-571
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• 2001

In this paper, we study selecting a transformation so that the transformed variable is nearly symmetrically distributed. The large sample properties of an M-estimator of transformation parameter that is obtained by minimizing the integrated square of the imaginary part of the empirical characteristic function are investigated when a random sample is selected from some unspecified distribution. According to influence function calculations and Monte Carlo simulations, these estimates are less sensitive, than the normal model maximum likelihood estimates, to a few outliers.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.213-224
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• 2014

In this paper, we study the problem of transforming to normality. We propose to estimate the transformation parameter by minimizing a weighted squared distance between the empirical characteristic function of transformed data and the characteristic function of the normal distribution. Our approach also allows for other symmetric target characteristic functions. Asymptotics are established for a random sample selected from an unknown distribution. The proofs show that the weight function $t^{-2}$ needs to be modified to have thinner tails. We also propose the method to compute the influence function for M-equation taking the form of U-statistics. The influence function calculations and a small Monte Carlo simulation show that our estimates are less sensitive to a few outliers than the maximum likelihood estimates.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.509-516
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• 2008

We derive the influence function on the coefficient of variation. Empirical influence function and Sample influence function are used to verify the validity of the derived influence function. To show the validity of the influence function, we carry out simulations with random samples from normal distribution $N(20,1^2)$ and $N(20,5^2)$, respectively. The simulation result proves that the derived influence function is very accurate in estimating changes in the coefficient of variation when an observation is deleted.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.33-42
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• 1999

A method of detecting influential observations in principal factor analysis is suggested. it is based on a perturbation of the empirical distribution function and an adoption of the local influence method. An illustrative example is given.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.181-188
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• 2000

The shape of an M-estimation function is generally determined in the sense of either/both maximizing efficiency of an M-estimator at the model or/and bounding the influence function of an M-estimator. We propose an empirical method of choosing a value of the bending constant in Huber's ${\psi}-function$, which is the most widely used M-estimation function when estimating the location parameter.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.252-263
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• 2011

In this work, for the first time, the quality of restoration in wide-field microscopy images after deconvolution was analyzed as a function of different Point Spread Functions using one deconvolution method, on a specimen of known size and on a biological specimen. The empirical Point Spread Function determination can significantly depend on the numerical aperture, refractive index of the embedding medium, refractive index of the immersion oil and cover slip thickness. The influence of all of these factors is shown in the same article and using the same microscope. We have found that the best deconvolution results are obtained when the empirical PSF utilized is obtained under the same conditions as the specimen. We also demonstrated that it is very important to quantitatively check the process' outcome using several quality indicators: Full-Width at Half-Maximum, Contrast-to-Noise Ratio, Signal-to-Noise Ratio and a Tenengrad-based function. We detected a significant improvement when using an indicator to measure the focus of the whole stack. Therefore, to qualitatively determinate the best deconvolved image between different conditions, one approach that we are pursuing is to use Tenengrad-based function indicators in images obtained using a wide-field microscope.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.31-64
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• 2009

This paper evaluates the role of fund distributors and the appropriateness of sales fees in funds by the empirical analysis of Korean equity funds. The empirical results are summarized as follows. First, this paper shows that the funds with higher sales fees do not have better performance. Rather, the higher sales fees cause the returns of funds to decrease in Korean equity funds. Second, it is not confirmed that both banks and securities firms, as fund distributors, contribute to the better performance of funds. Especially, the banks gave more negative influence on the performance of funds by imposing higher sales fees in funds than the securities firms. The empirical results suggest that the sales fees of funds are unduly imposed in comparison to the function of fund distributors and therefore, the structure of fund fees should be improved for the benefit of fund investors.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.156-162
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• 2003

The shock absorber is a part having a direct influence on the ride comfort, stability and dynamic load prediction of a vehicle. Thus, a rationally modeled shock absorber should be required in the dynamic analysis of vehicles. This thesis presents a modified model, based on Worden's hyperbolic tangent function, in order to fit experimental data on the velocity-damping force of a shock absorber. The hyperbolic tangent function correctly indicates the characteristics of a shock absorber, and has the advantage of containing physical causality. To evaluate the method, comparative evaluations of the linear model, the 5th polynomial model and Worden's model were carried out. The function presented in this paper is not only simple but also makes it possible to estimate the function coefficients easily and visually. In addition, it has the advantage of containing physical causality. Lastly, it effectively models the damping force of a shock absorber.