• 제목/요약/키워드: kernel distribution estimation

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단조 서포트벡터기계를 이용한 카플란-마이어 생존함수의 평활 (Smoothing Kaplan-Meier estimate using monotone support vector regression)

  • 황창하;심주용
    • Journal of the Korean Data and Information Science Society
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    • 제23권6호
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    • pp.1045-1054
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    • 2012
  • 서포트벡터 기계는 분류 및 비선형 함수추정에서 유용하게 사용되고 있는 통계적 기법이다. 본 논문에서는 두 개의 입력변수와 회귀함수의 단조 관계를 이용하여 단조 서포트벡터기계를 제안하고, Kaplan-Meier의 방법에 의해서 생존함수의 추정값이 주어진 경우 제안된 방법을 이용하여 생존 함수를 평활하는 방법 또한 제안한다. 모의실험에서는 실제 생존함수를 이용하여 Kaplan-Meier의 방법에 의한 생존함수의 추정값과의 성능을 비교함으로써 제안된 방법의 우수성을 보이기로 한다.

An efficient reliability analysis strategy for low failure probability problems

  • Cao, Runan;Sun, Zhili;Wang, Jian;Guo, Fanyi
    • Structural Engineering and Mechanics
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    • 제78권2호
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    • pp.209-218
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    • 2021
  • For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.

A Study on Goodness-of-fit Test for Density with Unknown Parameters

  • Hang, Changkon;Lee, Minyoung
    • Communications for Statistical Applications and Methods
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    • 제8권2호
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    • pp.483-497
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    • 2001
  • When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

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On the Plug-in Bandwidth Selectors in Kernel Density Estimation

  • Park, Byeong-Uk
    • Journal of the Korean Statistical Society
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    • 제18권2호
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    • pp.107-117
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    • 1989
  • A stronger result than that of Park and Marron (1994) is proved here on the asymptotic distribution of the plug-in bandwidth selector. The new result is that the plug-in bandwidth selector may have the rate of convergence ($n^{-4/13}$ with less smoothness conditions on the unknown density functions than as described in Park and Marron's paper. Together with this, a class of various plug-in bandwidth selectors are considered and their asymptotic distributions are given. Finally, some ideas of possible improvements on those plug-in bandwidth selectors are provided.

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Bootstrap methods for long-memory processes: a review

  • Kim, Young Min;Kim, Yongku
    • Communications for Statistical Applications and Methods
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    • 제24권1호
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    • pp.1-13
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    • 2017
  • This manuscript summarized advances in bootstrap methods for long-range dependent time series data. The stationary linear long-memory process is briefly described, which is a target process for bootstrap methodologies on time-domain and frequency-domain in this review. We illustrate time-domain bootstrap under long-range dependence, moving or non-overlapping block bootstraps, and the autoregressive-sieve bootstrap. In particular, block bootstrap methodologies need an adjustment factor for the distribution estimation of the sample mean in contrast to applications to weak dependent time processes. However, the autoregressive-sieve bootstrap does not need any other modification for application to long-memory. The frequency domain bootstrap for Whittle estimation is provided using parametric spectral density estimates because there is no current nonparametric spectral density estimation method using a kernel function for the linear long-range dependent time process.

Estimating Discriminatory Power with Non-normality and a Small Number of Defaults

  • Hong, C.S.;Kim, H.J.;Lee, J.L.
    • 응용통계연구
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    • 제25권5호
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    • pp.803-811
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    • 2012
  • For credit evaluation models, we extend the study of discriminatory power based on AUC obtained from a ROC curve when the number of defaults is small and distribution functions of the defaults and non-defaults are normal distributions. Since distribution functions do not satisfy normality in real world, the distribution functions of the defaults and non-defaults are assumed as normal mixture distributions based on results that the normal mixture could be better fitted than other distribution estimation methods for non-normal data. By using several AUC statistics, the discriminatory power under such a circumstance is explored and compared with those of normal distributions.

대기오염도의 공간적 분포 변화 분석 -수도권 지역을 대상으로- (Spatial Distributions of the Ambient Levels of Air Pollutants in Seoul Metropolitan Area)

  • 권오상;안동환;김원희
    • 자원ㆍ환경경제연구
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    • 제13권1호
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    • pp.83-117
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    • 2004
  • 본 연구는 수도권지역 대기오염 측정망의 오염물질별 연평균 오염도 측정치를 이용하여 최근 10년간의 수도권내 대기오염도의 공간적 분포 및 그 변화를 분석하였다. 분석을 위해 커널확률밀도함수를 추정하고, 또한 지니계수와 엔트로피계열의 불평등지수를 계측하였으며, 분석기간중 오염도의 공간적 분포 변화에 대한 통계적 검정을 실시하였다. 분석결과 최근 10년간 수도권 지역의 $SO_2$, $NO_2$, $O_3$와 CO 등 대기오염물질 오염도의 지역격차는 대체로 일정한 수준을 유지하거나 아니면 완화되는 것으로 나타났다. 또한 순위상관 분석 결과 분석기간 중 오염물질의 공간적 분포에 상당한 정도의 동태적 변화가 있었던 것으로 나타났다.

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Analysis of ASEAN's Stock Returns and/or Volatility Distribution under the Impact of the Chinese EPU: Evidence Based on Conditional Kernel Density Approach

  • Mohib Ur Rahman;Irfan Ullah;Aurang Zeb
    • East Asian Economic Review
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    • 제27권1호
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    • pp.33-60
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    • 2023
  • This paper analyzes the entire distribution of stock market returns/volatility in five emerging markets (ASEAN5) and figures out the conditional distribution of the CHI_EPU index. The aim is to examine the impact of CHI_EPU on the stock returns/volatility density of ASEAN5 markets. It also examined whether changes in CHI_EPU explain returns at higher or lower points (abnormal returns). This paper models the behaviour of stock returns from March 2011 to June 2018 using a non-parametric conditional density estimation approach. The results indicate that CHI_EPU diminishes stock returns and augments volatility in ASEAN5 markets, except for Malaysia, where it affects stock returns positively. The possible reason for this positive impact is that EPU is not the leading factor reducing Malaysian stock returns; but, other forces, such as dependency on other countries' stock markets and global factors, may have a positive impact on stock returns (Bachmann and Bayer, 2013). Thus, the risk of simultaneous investment in Chinese and ASEAN5 stock markets, except Malaysia, is high. Further, the degree of this influence intensifies at extreme high/low intervals (positive/negative tails). The findings of this study have significant implications for investors, policymakers, market agents, and analysts of ASEAN5.

모수적·비모수적 입력모델링 기법을 이용한 신뢰성 해석 (Reliability Analysis Using Parametric and Nonparametric Input Modeling Methods)

  • 강영진;홍지민;임오강;노유정
    • 한국전산구조공학회논문집
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    • 제30권1호
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    • pp.87-94
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    • 2017
  • 신뢰성 해석 및 신뢰성기반 최적설계는 불확실성을 고려한 확률변수를 입력 값으로 요구하며, 확률변수는 모수적 비모수적 통계모델링 방법을 사용하여 확률분포함수의 형태로 정량화 된다. 신뢰성 해석과 같은 통계적 해석은 입력되는 확률분포함수의 특성이 결과값에 영향을 미치게 되며, 확률분포함수는 통계모델링 방법에 따라 다른 형태를 가지게 된다. 본 연구에서는 모수적 통계모델링 방법인 순차적 통계모델링 방법과 비모수적 방법인 커널밀도추정을 사용하여 데이터의 개수에 따른 통계모델링의 결과를 분석하였다. 또한 수치예제를 통해 두 가지 기법에 따른 신뢰성 해석의 결과를 분석하였고, 데이터의 개수에 따른 적절한 기법을 제안하였다.

Fluctuation in operational energy efficiency of ships and its implications for performance appraisal

  • Zhang, Shuang;Yuan, Haichao;Sun, Deping
    • International Journal of Naval Architecture and Ocean Engineering
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    • 제13권1호
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    • pp.367-378
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    • 2021
  • This paper develops a dynamic regression model to quantify the contribution of key external factors to operational energy efficiency of ships. On this basis, kernel density estimation is applied to explore distribution patterns of fluctuations in operational performance. An empirical analysis based on these methods show that distribution of fluctuations in Energy Efficiency Operational Indicator (EEOI) is leptokurtic and fat tailed, rather than a normal one. Around 85% of fluctuations in EEOI can be jointly explained by capacity utilization and sailing speed, while the rest depend on other external factors largely beyond control. The variations in capacity utilization and sailing speed cannot be fully passed on to the energy efficiency performance of ships, due to complex interactions between various external factors. The application of the methods is demonstrated, showing a potential approach to develop a rating mechanism for use in the legally binding framework on operational energy efficiency of ships.