• Title, Summary, Keyword: influence function

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INFLUENCE ANALYSIS FOR GENERALIZED ESTIMATING EQUATIONS

  • Jung Kang-Mo
    • 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.

Influence Analysis in Selecting Discriminant Variables

  • Jung, Kang-Mo;Kim, Myung-Geun
    • 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.

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Influence of an Observation on the t-statistic

  • Kim, Hong-Gie;Kim, Kyung-Hee
    • 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.

Influence Function on the Coefficient of Variation (변이계수에 대한 영향함수)

  • Lee, Yun-Hee;Kim, Hong-Gie
    • 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.

Factors that Influence Satisfaction of Shoppers Who have Internet Shopping Mall Experience. (인터넷 쇼핑몰 구매경험자들의 고객만족도에 영향을 미치는 요인)

  • Kim, Sung-Eon
    • 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.

Influence Function on Tolerance Limit

  • Kim, Honggie;Lee, Yun Hee;Shin, Hee Sung;Lee, Sounki
    • 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.

Analysis of Load Transmission Characteristics for Automobile Helical Gear (자동차 헬리컬기어의 하중전달 특성해석)

  • Park, C.I.;Lee, J.M.
    • 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.

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OUTLIER DETECTION BASED ON A CHANGE OF LIKELIHOOD

  • Kim, Myung-Geun
    • 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.

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Influence in Fitting an Equicorrelation Model

  • Kim, Myung Geun;Jung, Kang-Mo
    • 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.

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Influence Measures for the Likelihood Ratio Test on Independence of Two Random Vectors

  • Jung, Kang-Mo
    • 한국데이터정보과학회:학술대회논문집
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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.

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