• Journal of Korea Game Society
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• v.7 no.4
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• pp.117-123
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• 2007

This paper presents the Stone Influence, the Influence Point, and the Influence Area on computer Go. The Stone Influence is defined using the distance between stone and empty point. The Influence Point is defined using threshold value on the Stone Influence. The Influence Area is defined using lump of the Influence Points and its Core. In experiments using the Jeongseok data, the author obtained the threshold of Influence Points. The proposed method was verified by experiments where it was success fully applied to the influence in game of Go.

• 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.

• 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.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.635-642
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• 2005

In statistical diagnostics a large number of influence measures have been proposed for identifying outliers and influential observations. However it seems to be few accounts of the influence diagnostics on test statistics. We study influence analysis on the likelihood ratio test statistic whether the two sets of variables are uncorrelated with one another or not. The influence of observations is measured using the case-deletion approach, the influence function. We compared the proposed influence measures through two illustrative examples.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.841-849
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• 2001

The influence in fitting an equicorrelation model is investigated using the influence function. The influence functions for the model parameters are derived and its sample versions are used for investigating the influence of observations on the estimators of the parameters. Some relationships among the sample versions are found. We will derive a measure for identifying observations that have a large influence on the test of fitting the equicorrelation model using the influence function method. An example is given for illustration.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.13-16
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• 2001

We compare methods for detecting influential observations that have a large influence on the likelihood ratio test statistics that the two sets of variables are uncorrelated with one another. For this purpose we derive results of the deletion diagnostic, the influence function, the standardized influence matrix and the local influence. An illustrative example is given.

• Journal of applied mathematics & informatics
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• v.28 no.3_4
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• pp.913-921
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• 2010

The derivative influence measure is adapted to the Cholesky decomposition of a covariance matrix. Formulas for the derivative influence of observations on the Cholesky root and the inverse Cholesky root of a sample covariance matrix are derived. It is easy to implement this influence diagnostic method for practical use. A numerical example is given for illustration.

• Korean Institute of Interior Design Journal
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• no.26
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• pp.57-63
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• 2001

The aim of this study is to measure sensitivity reaction of human being with a physical properties of color and average size of particle for building materials and predict the influence of color and average size of particle in evaluation of building materials. As a results, 1) In results of qualitative evaluation construction, all 16 adjectives were extracted by higher evaluation items and ,total 14 adjectives were extracted as evaluation adjectives except adjectives of a contrary concept in each other. 2) According to the result of factor analysis, all 4 group of $\ulcirner$potency$\lrcirner$, $\ulcirner$activity$\lrcirner$, $\ulcirner$evaluation$\lrcirner$, $\ulcirner$warmness$\lrcirner$ were extracted. In this time, $\ulcirner$potency$\lrcirner$ as the first factor indicates the most hign original value. Consequently, $\ulcirner$potency$\lrcirner$ factor have an hign influence in evaluation of building materials. 3) As a influence analysis of evaluation variable by evaluation item $\ulcirner$potency$\lrcirner$ factor have an high influence by influence of average size of a particle, $\ulcirner$activity$\lrcirner$ factor have influence hignly by influence of brightness, $\ulcirner$evaluation$\lrcirner$ factor have an hign influence by influence of average site of a particle and the hue, $\ulcirner$warmness$\lrcirner$ factor have an hign influence mainly by influence of the hue

• Journal of Digital Convergence
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• v.14 no.10
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• pp.137-145
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• 2016

The explosive growth of social networking services based on smart devices popularize these relationships and activities online in accordance with the far larger impact of this on the real life offline, the interest and importance for the online activity is increasing. In this study, factors affecting the SNS activity are defined by object, user, influence direction, influence distance and proposed a method to measure organic terms in effect between the SNS users. Influence Direction and Influence Strength (or Distance) are elaborated by using the existing influence measurement element such as structured data - the number of friends, the difference between the number of contacts - and the new influence measurement element such as unstructured data - gap between the former time and the latter time, preference and type of response behavior - that occur in social network service. In addition, the system for collecting and analysing data for measuring influence from social network service and the process model on the method for measuring influence is tested by using sample data on Facebook and explained the implementation probability.