• 제목/요약/키워드: truncated distribution

검색결과 151건 처리시간 0.037초

치우친 다변량 t-분포 혼합모형에 대한 최우추정 (An Alternating Approach of Maximum Likelihood Estimation for Mixture of Multivariate Skew t-Distribution)

  • 김승구
    • 응용통계연구
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    • 제27권5호
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    • pp.819-831
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    • 2014
  • 치우친 다변량 t-분포 혼합을 적합하기 위해 Exact-EM 알고리즘이 사용된다. 그러나 이 방법은 E-step에서 매우 긴 처리시간을 요하는 다변량 절단 t-분포의 적률을 계산해야 한다. 본 논문에서는 이러한 문제점을 완화하기 위해 SPU-EM이라 명명한 알고리즘을 제안하는데, 이것은 Meng과 van Dyk (1997)의 AECM 알고리즘의 원리를 이용하여 다차원 적률의 계산상의 어려움을 해결한다. 결과적으로 제안된 방법은 Exact-EM 알고리즘 보다 빠른 처리시간으로 보장한다. 이를 입증하기 위해 실험을 통해 제안된 방법의 유효성을 보인다.

1차원 유한요소망 연속기법을 이용한 시간영역 탄성파의 역해석 (Time-domain Elastic Full-waveform Inversion Using One-dimensional Mesh Continuation Scheme)

  • 강준원
    • 한국전산구조공학회논문집
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    • 제26권4호
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    • pp.213-221
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    • 2013
  • 이 논문에서는 반무한 고체영역의 표면에서 측정한 변위응답의 시간이력으로부터 유한요소망 연속기법을 이용해 탄성파 속도의 공간적 분포를 추정하는 역해석 문제를 소개한다. 반무한 영역에서의 역해석을 위해서는 해석 대상이 되는 유한영역의 경계에서 파동의 반사가 일어나지 않도록 하는 것이 중요하다. 이를 위해 유한영역의 경계면에 perfectly-matchedlayers(PMLs)라는 수치적 파동흡수층을 도입하였고, PML을 경계로 하는 유한영역에서 역해석 문제를 정의하였다. 이 문제를 탄성파동방정식을 구속조건으로 하는 최적화 문제로 표현하였으며, 라그랑주 승수법에 기초한 비구속 최적화 기법에 의해 탄성파속도의 최적 분포를 결정하였다. 해의 정확도와 수렴성을 높이기 위해 유한요소망 연속기법을 도입하여 점진적으로 밀도가 증가하는 유한요소망에 대해 연속적으로 역해석을 수행하였다. 1차원 예제들을 통해 유한요소망 연속기법을 이용한 역해석으로부터 탄성파속도의 분포를 정확히 추정할 수 있음을 확인하였으며, 측정 응답에 노이즈가 존재하는 경우에도 제안한 역해석 기법은 목표 탄성파속도 분포에 근사한 결과를 도출하였다.

A Study on How General Super Markets Affect Traditional Markets Performance

  • Yoo, Byong-Kook;Kim, Soon-Hong
    • 유통과학연구
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    • 제15권11호
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    • pp.49-57
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    • 2017
  • Purpose - In Korea, general super markets have a great impact on the market performance of traditional markets. We propose a modified two stage DEA model for evaluating the performance of traditional markets in Incheon, Korea by identifying the influence of external environmental factors including the presence of general super markets as non-discretionary variables in DEA. Research design, data, and methodology - After obtaining bias-corrected estimates of original DEA efficiency scores using the input and output data of 49 traditional markets, we regress them on several external environmental factors by bootstrap-truncated regression. Results - We obtain bias-corrected efficiency scores from the original DEA efficiency scores by bootstrap and among the five environmental factors, the residential population and the presence of general super markets or SSMs can be considered as the driving forces influencing bias-corrected efficiency scores, positively and negatively, respectively. Conclusions - When DEA efficiency scores tend to be overestimated, we need to use a biased-corrected efficiency score by bootstrap. It is important to note that the efficiency of traditional markets can be largely influenced by external environmental factors such as the presence of general super markets or SSMs that traditional markets can not control. Therefore, it is desirable to consider such environmental factors appropriately for a reasonable performance evaluation.

Determination of subcellular localization of Betanodavirus B2

  • 김영미;차승주;문창훈;도정완;박정우
    • 한국양식학회:학술대회논문집
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    • 한국양식학회 2006년도 수산관련학회 공동학술대회 발표요지집
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    • pp.476-478
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    • 2006
  • To analyze subcellular localization of betanodavirus protein B2, a plasmid expressing Betanodavirus protein B2 fused to enhanced green fluorescent protein (EGFP-Nl) was constructed. The transient expression of full-length B2 fused to EGFP in GF cells confirmed the equal distribution of protein B2 between cytoplasm and nucleus. However, transfection of N-terminal half of the B2 revealed that this truncated form predominantly localized to the cytoplasm. By using several deletion mutants and point mutants, we determined the regions and/or motif responsible for the subcellular localization of betanodavirus.

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Korean Welfare Panel Data: A Computational Bayesian Method for Ordered Probit Random Effects Models

  • Lee, Hyejin;Kyung, Minjung
    • Communications for Statistical Applications and Methods
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    • 제21권1호
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    • pp.45-60
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    • 2014
  • We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

평활(平滑) 모수(母數) 선택(選擇)에 기준(基準)한 적합도(適合度) 검정(檢定) (Goodness-of-Fit Test Based on Smoothing Parameter Selection Criteria)

  • 김종태
    • Journal of the Korean Data and Information Science Society
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    • 제4권
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    • pp.137-146
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    • 1993
  • The Proposed goodness-of-fit test Statistic $\hat{\lambda}_{\alpha}$ derived from the test Statistc in Kim (1992) is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator, $d_{\hat{\lambda}{n}}$, of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function in the event that $H_{0}$ is ejected. The limiting distribution of $\hat{\lambda}_{\alpha}$ was obtained under the null hypothesis. It is also shown that this test is consistent against fixed alternatives.

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A General Class of Acceptance-Rejection Distributions and Its Applications

  • 김혜중;염준근;이영섭;조천호;정효상
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2003년도 추계학술대회
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    • pp.19-30
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    • 2003
  • In this paper we present a new family of distributions that allows a continuous variation not only from normality to non-normality but also from unimodality to bimodality. Its properties are especially useful in studying and making inferences about models involving the univariate truncated normal distribution. The properties of the family and its applications are given.

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Probabilistic Modeling of Fiber Length Segments within a Bounded Area of Two-Dimensional Fiber Webs

  • Chun, Heui-Ju
    • Communications for Statistical Applications and Methods
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    • 제18권3호
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    • pp.301-317
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    • 2011
  • Statistical and probabilistic behaviors of fibers forming fiber webs of all kinds are of great significance in the determination of the uniformity and physical properties of the webs commonly found in many industrial products such as filters, membranes and non-woven fabrics. However, in studying the spatial geometry of the webs the observations must be theoretically as well as experimentally confined within a specified unit area. This paper provides a general theory and framework for computer simulation for quantifying the fiber segments bounded by the unit area in consideration of the "edge effects" resulting from the truncated length segments within the boundary. The probability density function and the first and second moments of the length segments found within the counting region were derived by properly defining the seeding region and counting region.

Multiple Change-Point Estimation of Air Pollution Mean Vectors

  • Kim, Jae-Hee;Cheon, Sooy-Oung
    • 응용통계연구
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    • 제22권4호
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    • pp.687-695
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    • 2009
  • The Bayesian multiple change-point estimation has been applied to the daily means of ozone and PM10 data in Seoul for the period 1999. We focus on the detection of multiple change-points in the ozone and PM10 bivariate vectors by evaluating the posterior probabilities and Bayesian information criterion(BIC) using the stochastic approximation Monte Carlo(SAMC) algorithm. The result gives 5 change-points of mean vectors of ozone and PM10, which are related with the seasonal characteristics.

A Bayesian Method for Narrowing the Scope fo Variable Selection in Binary Response t-Link Regression

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제29권4호
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    • pp.407-422
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    • 2000
  • This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

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