• 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.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.213-224
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• 2006

We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations using the influence function and the derivative influence measures. The influence function for regression coefficients is derived and its sample versions are used for influence analysis. The derivative influence measures under certain perturbation schemes are derived. It can be seen that the influence function method and the derivative influence measures yield the same influence information. An illustrative example in longitudinal data analysis is given and we compare the results provided by the influence function method and the derivative influence measures.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.499-509
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• 2001

We investigate the influence of observations on a test of additional information about discrimination using the influence function and the derivative influence measures. the influence function for the test statistic is derived and this sample versions are used for influence analysis. The derivative influence measures for the test statistic under a perturbation scheme are derived. It will be seen that the influence function method and the derivative influence measures yield the same result. Furthermore, we will derive the relationships between the influence function and the derivative influence measures when the sample size is large. an illustrative example is given and we will compare the results provided by the influence function method and the derivative influence measures.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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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.

• 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.

• 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.

• The Journal of Information Systems
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• v.17 no.2
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• pp.27-47
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• 2008

The base of an internet shopping mall is web application system. However, this mall is a market place where selling and buying of products and services take place. Therefore, it should be considered both a commerce function and a web site design function. The commerce function and the web site design function are considered as variables that influence trust and familiarity. This trust and familiarity are considered as the main factors that influence satisfaction of shoppers of internet shopping malls. Analysis indicates that the web site design function significantly influences both trust and familiarity, but the commerce function significantly influence only familiarity negatively. Also trust and familiarity significantly influence user satisfaction.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.497-505
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• 2003

Under normality assumption, the tolerance interval for a future observation is sometimes of great interest in statistics. In this paper, we state the influence function on the standard deviation $\sigma$, and use it to derive the influence function on tolerance limits. Simulation study shows that the two influence functions perform very well.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.5
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• pp.1-9
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• 1995

The purpose of this study is to develop a computer simulation program for analyzing load transmission characteristics of a helical gear system in design stage. In this analysis, the rotational delay, load distribution, root stress, and contact area are investigated. That is, the influence function of deflection is obtained by finite element analysis and the influence function of approach and gear tooth error are considered. Load distribution, rotational delay, and contact area are calculated by solving load-deflection equation which includes these influence functions and tooth error, and the influence function of the bending moment is obtained by finite element analysis. The root stress is calculated by the load distribution and the influence function of the bending moment. The results of the simulation are cross-checked through a specially designed experimental set-up.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.1133-1138
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• 2008

A general method of detecting outliers based on a change of likelihood by using the influence function is suggested. It can be applied to all kinds of distributions that are specified by parameters. For the multivariate normal case, specific computations are made to get the corresponding conditional influence function. A numerical example is provided for illustration.