• 제목/요약/키워드: Linear log

검색결과 623건 처리시간 0.02초

Outlying Cell Identification Method Using Interaction Estimates of Log-linear Models

  • Hong, Chong Sun;Jung, Min Jung
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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    • pp.291-303
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    • 2003
  • This work is proposed an alternative identification method of outlying cell which is one of important issues in categorical data analysis. One finds that there is a strong relationship between the location of an outlying cell and the corresponding parameter estimates of the well-fitted log-linear model. Among parameters of log-linear model, an outlying cell is affected by interaction terms rather than main effect terms. Hence one could identify an outlying cell by investigating of parameter estimates in an appropriate log-linear model.

On the growth of entire functions satisfying second order linear differential equations

  • Kwon, Ki-Ho
    • 대한수학회보
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    • 제33권3호
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    • pp.487-496
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    • 1996
  • Let f(z) be an entire function. Then the order $\rho(f)$ of f is defined by $$ \rho(f) = \overline{lim}_r\to\infty \frac{log r}{log^+ T(r,f)} = \overline{lim}_r\to\infty \frac{log r}{log^+ log^+ M(r,f)}, $$ where T(r,f) is the Nevanlinna characteristic of f (see [4]), $M(r,f) = max_{$\mid$z$\mid$=r} $\mid$f(z)$\mid$$ and $log^+ t = max(log t, 0)$.

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A LARGE-UPDATE INTERIOR POINT ALGORITHM FOR $P_*(\kappa)$ LCP BASED ON A NEW KERNEL FUNCTION

  • Cho, You-Young;Cho, Gyeong-Mi
    • East Asian mathematical journal
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    • 제26권1호
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    • pp.9-23
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    • 2010
  • In this paper we generalize large-update primal-dual interior point methods for linear optimization problems in [2] to the $P_*(\kappa)$ linear complementarity problems based on a new kernel function which includes the kernel function in [2] as a special case. The kernel function is neither self-regular nor eligible. Furthermore, we improve the complexity result in [2] from $O(\sqrt[]{n}(\log\;n)^2\;\log\;\frac{n{\mu}o}{\epsilon})$ to $O\sqrt[]{n}(\log\;n)\log(\log\;n)\log\;\frac{m{\mu}o}{\epsilon}$.

로지스틱회귀모형에서 로그-밀도비를 이용한 변수의 선택 (Variable Selection with Log-Density in Logistic Regression Model)

  • 강명욱;신은영
    • Communications for Statistical Applications and Methods
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    • 제19권1호
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    • pp.1-11
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    • 2012
  • 로지스틱회귀모형에서 반응변수가 주어졌을 때 설명변수의 조건부 확률분포의 로그-밀도비는 어떤 설명변수가어떻게모형에포함되는지에대한변수선택문제에서유용한정보를제공한다. 설명변수의 조건부 확률분포가 좌우대칭이 아닌 경우 감마분포로 가정하는 것이 적절하다. 여러 가지 모의실험을 수행한 결과를 보면, $x{\mid}y$ = 0과 $x{\mid}y$ = 1의 두 분포가 겹치는 경우에서는 x항과 log(x)항 모두 필요하다. 그리고 두 분포가 분리된 경우에는 x항 또는 log(x)항 중 하나만 필요하다.

가속화 수명 실험에서의 비모수적 추론 (Nonparametric Inference for Accelerated Life Testing)

  • 김태규
    • 품질경영학회지
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    • 제32권4호
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    • pp.242-251
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    • 2004
  • Several statistical methods are introduced 1=o analyze the accelerated failure time data. Most frequently used method is the log-linear approach with parametric assumption. Since the accelerated failure time experiments are exposed to many environmental restrictions, parametric log-linear relationship might not be working properly to analyze the resulting data. The models proposed by Buckley and James(1979) and Stute(1993) could be useful in the situation where parametric log-linear method could not be applicable. Those methods are introduced in accelerated experimental situation under the thermal acceleration and discussed through an illustrated example.

A Simulation Approach for Testing Non-hierarchical Log-linear Models

  • Park, Hyun-Jip;Hong, Chong-Sun
    • Communications for Statistical Applications and Methods
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    • 제6권2호
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    • pp.357-366
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    • 1999
  • Let us assume that two different log-linear models are selected by various model selection methods. When these are non-hierarchical it is not easy to choose one of these models. In this paper the well-known Cox's statistic is applied to compare these non-hierarchical log-linear models. Since it is impossible to obtain the analytic solution about the problem we proposed a alternative method by extending Pesaran and pesaran's (1993) simulation approach. We find that the values of proposed test statistic and the estimates are very much stable with some empirical results.

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Suppression and Collapsibility for Log-linear Models

  • Sun, Hong-Chong
    • Communications for Statistical Applications and Methods
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    • 제11권3호
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    • pp.519-527
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    • 2004
  • Relationship between the partial likelihood ratio statistics for logisitic models and the partial goodness-of-fit statistics for corresponding log-linear models is discussed. This paper shows how definitions of suppression in logistic model can be adapted for log-linear model and how they are related to confounding in terms of collapsibility for categorical data. Several $2{times}2{times}2$ contingency tables are illustrated.

Graphical Methods for Hierarchical Log-Linear Models

  • Hong, Chong-Sun;Lee, Ui-Ki
    • Communications for Statistical Applications and Methods
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    • 제13권3호
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    • pp.755-764
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    • 2006
  • Most graphical methods for categorical data can describe the structure of data and represent a measure of association among categorical variables. Among them the polyhedron plot represents sequential relationships among hierarchical log-linear models for a multidimensional contingency table. This kind of plot could be explored to describe the differences among sequential models. In this paper we suggest graphical methods, containing all the information, that reflect the relationship among all log-linear models in a certain hierarchical structure. We use the ideas of a correlation diagram.

AN ELIGIBLE PRIMAL-DUAL INTERIOR-POINT METHOD FOR LINEAR OPTIMIZATION

  • Cho, Gyeong-Mi;Lee, Yong-Hoon
    • East Asian mathematical journal
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    • 제29권3호
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    • pp.279-292
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    • 2013
  • It is well known that each kernel function defines a primal-dual interior-point method(IPM). Most of polynomial-time interior-point algorithms for linear optimization(LO) are based on the logarithmic kernel function([2, 11]). In this paper we define a new eligible kernel function and propose a new search direction and proximity function based on this function for LO problems. We show that the new algorithm has ${\mathcal{O}}((log\;p){\sqrt{n}}\;log\;n\;log\;{\frac{n}{\epsilon}})$ and ${\mathcal{O}}((q\;log\;p)^{\frac{3}{2}}{\sqrt{n}}\;log\;{\frac{n}{\epsilon}})$ iteration bound for large- and small-update methods, respectively. These are currently the best known complexity results.