• 제목/요약/키워드: Variable Statistics

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Simultaneous outlier detection and variable selection via difference-based regression model and stochastic search variable selection

  • Park, Jong Suk;Park, Chun Gun;Lee, Kyeong Eun
    • Communications for Statistical Applications and Methods
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    • 제26권2호
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    • pp.149-161
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    • 2019
  • In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.

대용변수를 이용한 $\bar{X}$ 관리도의 경제적 설계 (Economic Design of $\bar{X}$ Control Chart Using a Surrogate Variable)

  • 이태훈;이재훈;이민구;이주호
    • 품질경영학회지
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    • 제37권2호
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    • pp.46-57
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    • 2009
  • The traditional approach to economic design of control charts is based on the assumption that a process is monitored using a performance variable. However, various types of automatic test equipments recently introduced as a part of factory automation usually measure surrogate variables instead of performance variables that are costly to measure. In this article we propose a model for economic design of a control chart which uses a surrogate variable that is highly correlated with the performance variable. The optimum values of the design parameters are determined by maximizing the total average income per cycle time. Numerical studies are performed to compare the proposed $\bar{X}$ control charts with the traditional model using the examples in Panagos et al. (1985).

랜덤포레스트를 위한 상관예측변수 중요도 (Correlated variable importance for random forests)

  • 신승범;조형준
    • 응용통계연구
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    • 제34권2호
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    • pp.177-190
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    • 2021
  • 랜덤포레스트는 여러 의사결정나무 모형들을 융합하여 안정성과 예측력을 높여주기 때문에 종종 사용되는 방법이다. 예측력을 증가시키는 반면 해석의 용이성을 희생하기 때문에 이를 보상하기 위해 변수의 중요도를 제공한다. 변수의 중요도는 랜덤포레스트를 구축할 때 변수가 얼마나 중요한 역할을 하는지를 알려 준다. 그러나 어떤 예측변수가 다른 예측변수들과 상관되어 있을 때 기존 알고리즘의 변수중요도는 왜곡될 수 있다. 상관된 예측변수들의 하향 편향은 예측변수의 중요도를 실제 중요도보다 낮게 측정하게 한다. 우리는 기존 알고리즘을 수정하여 상관 예측변수의 하향 편향을 회복하는 새로운 알고리즘을 제안한다. 제안된 알고리즘의 성능은 모의 자료에 의해 증명되고 실제 자료에 의해 설명된다.

On Estimating of Kullback-Leibler Information Function using Three Step Stress Accelerated Life Test

  • Park, Byung-Gu;Yoon, Sang-Chul;Cho, Ji-Young
    • International Journal of Reliability and Applications
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    • 제1권2호
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    • pp.155-165
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    • 2000
  • In this paper, we propose some estimators of Kullback- Leibler Information functions using the data from three step stress accelerated life tests. This acceleration model is assumed to be a tampered random variable model. Some asymptotic properties of proposed estimators are proved. Simulations are performed for comparing the small sample properties of the proposed estimators under use condition of accelerated life test.

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Biplots of Multivariate Data Guided by Linear and/or Logistic Regression

  • Huh, Myung-Hoe;Lee, Yonggoo
    • Communications for Statistical Applications and Methods
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    • 제20권2호
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    • pp.129-136
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    • 2013
  • Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

Estimation of Median in the Presence of Three Known Quartiles of an Auxiliary Variable

  • Singh, Housila P.;Shanmugam, Ramalingam;Singh, Sarjinder;Kim, Jong-Min
    • Communications for Statistical Applications and Methods
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    • 제21권5호
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    • pp.363-386
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    • 2014
  • This paper has improved several ratio type estimators of the population median including their generalization in the presence of three known quartiles of an auxiliary variable. The properties of the improved estimators are discussed and applied. Both the empirical and simulation studies confirm that our new estimators perform efficiently.

SUBNORMALITY OF S2(a, b, c, d) AND ITS BERGER MEASURE

  • Duan, Yongjiang;Ni, Jiaqi
    • 대한수학회보
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    • 제53권3호
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    • pp.943-957
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    • 2016
  • We introduce a 2-variable weighted shift, denoted by $S_2$(a, b, c, d), which arises naturally from analytic function space theory. We investigate when it is subnormal, and compute the Berger measure of it when it is subnormal. And we apply the results to investigate the relationship among 2-variable subnormal, hyponormal and 2-hyponormal weighted shifts.

Properties of variable sampling interval control charts

  • Chang, Duk-Joon;Heo, Sun-Yeong
    • Journal of the Korean Data and Information Science Society
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    • 제21권4호
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    • pp.819-829
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    • 2010
  • Properties of multivariate variable sampling interval (VSI) Shewhart and CUSUM charts for monitoring mean vector of related quality variables are investigated. To evaluate average time to signal (ATS) and average number of switches (ANSW) of the proposed charts, Markov chain approaches and simulations are applied. Performances of the proposed charts are also investigated both when the process is in-control and when it is out-of-control.

Variable Selection Theorems in General Linear Model

  • 박정수;윤상후
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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    • pp.171-179
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    • 2006
  • For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the underfitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model.

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