• 제목/요약/키워드: Density estimation

검색결과 1,214건 처리시간 0.026초

A Note on Central Limit Theorem for Deconvolution Wavelet Density Estimators

  • Lee, Sungho
    • Communications for Statistical Applications and Methods
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    • 제9권1호
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    • pp.241-248
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    • 2002
  • The problem of wavelet density estimation based on Shannon's wavelets is studied when the sample observations are contaminated with random noise. In this paper we will discuss the asymptotic normality for deconvolving wavelet density estimator of the unknown density f(x) when courier transform of random noise has polynomial descent.

어림 활동이 문제 해결 과정에서 개념 이해, 해답 예측, 계산에 미치는 영향 : 속력과 밀도의 사례를 중심으로 (The Effects of Estimation Activities on Understanding Concepts, Predicting and Calculating Answers in Problem Solving Procedure: Cases of Speed and Density)

  • 서정아;조광희;송진웅;박승재
    • 한국과학교육학회지
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    • 제24권5호
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    • pp.814-824
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    • 2004
  • 본 연구는 속력과 밀도 개념에 대하여 어림활동을 실시하고, 학생들의 문제 해결과정에 미친 효과를 분석하였다. 그리고 사례 분석을 통하여 어림활동이 밀도 문제 해결과정에 주는 영향을 미시적으로 살펴보았다. 연구 대상은 중학교 1학년 남학생 192명이었다. 어림활동반의 세 명의 학생이 면담과 활동 녹취를 하였다. 연구 결과 어림활동과 측정활동이 모두 밀도 개념에 대한 정성적인 이해와 계산 능력에 도움을 주었으나, 계산 문제의 해답을 예측하는 능력은 어림활동반에서만 유의미하게 향상하였다. 사례 분석 결과, 어림활동은 학생들이 밀도의 관계식과 밀도 값을 경험세계와 관련지어 이해할 수 있도록 도와주었으며 이와 같은 이해를 바탕으로 계산 문제의 해답을 예측하도록 도왔다. 그리고 계산 능력이 부족한 한 학생은 계산 문제를 정성적으로 이해하여 답이 어떻게 되어야 할지를 말할 수 있었다. 결론적으로 어림활동은 개념과 관련된 관계식이나 값들을 경험적으로 이해하도록 도와 문제를 해결하는 과정에서 해답을 예측하도록 하였으며, 수학적인 능력이 부족한 학생이 계산 문제를 이해하는 데에 도움이 되었다.

영역별 절점 재분포를 통한 사면체 격자 재구성 방법 및 유한요소해석에의 적용 (A New Remeshing Technique of Tetrahedral Elements by Redistribution of Nodes in Subdomains and its Application to the Finite Element Analysis)

  • 홍진태;이석렬;양동열
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2005년도 춘계학술대회 논문집
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    • pp.607-610
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    • 2005
  • A remeshing algorithm using tetrahedral elements has been developed, which is adapted to the mesh density map constructed by a posteriori error estimation. In the finite element analyses of metal forging processes, numerical error increases as deformation proceeds due to severe distortion of elements. In order to reduce the numerical error, the desired mesh sizes in each region of the workpiece are calculated by a posteriori error estimation and the density map is constructed. Piecewise density functions are then constructed with the radial basis function in order to interpolate the discrete data of the density map. The sample mesh is constructed based on the point insertion technique which is adapted to the density function and the mesh size is controlled by moving and deleting nodes to obtain optimal distribution according to the mesh density function and the quality optimization function as well. After finishing the redistribution process of nodes, a tetrahedral mesh is constructed with the redistributed nodes, which is adapted to the density map and resulting in good mesh quality. A goodness and adaptability of the constructed mesh is verified with a testing measure. The proposed remeshing technique is applied to the finite element analyses of forging processes.

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Modified Local Density Estimation for the Log-Linear Density

  • Pak, Ro-Jin
    • Communications for Statistical Applications and Methods
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    • 제7권1호
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    • pp.13-22
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    • 2000
  • We consider local likelihood method with a smoothed version of the model density in stead of an original model density. For simplicity a model is assumed as the log-linear density then we were able to show that the proposed local density estimator is less affected by changes among observations but its bias increases little bit more than that of the currently used local density estimator. Hence if we use the existing method and the proposed method in a proper way we would derive the local density estimator fitting the data in a better way.

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ROC 함수 추정 (ROC Function Estimation)

  • 홍종선;;홍선우
    • 응용통계연구
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    • 제24권6호
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    • pp.987-994
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    • 2011
  • 모집단이 부도와 정상상태로 구분되는 신용평가 관점에서 부도와 정상 상태의 조건부 누적분포함수를 추정하는 방법으로 정규혼합 분포추정과 kernel density estimation을 이용하는 분포추정을 고려한다. 정규혼합 분포의 모수를 EM 알고리즘을 사용해 추정하고, KDE 방법에서는 많이 사용하는 다섯 종류의 커널 함수와 네가지의 띠폭을 이용한다. 그리고 추정한 분포로부터 구한 각각의 ROC 함수를 구한다. 추정한 분포들의 적합도를 비교 분석하고, 이를 바탕으로 구한 ROC 곡선의 성과를 비교 토론한다. 본 연구에서는 KDE 방법으로 추정한 분포함수가 더 적합하고, 추정한 정규혼합 분포를 이용한 ROC 함수가 더 좋은 성과를 나타내는 것을 발견하였다.

자체검정 번들조정법에 있어서 최적 ROBUST추정법의 결정 (DETERMINATION OF OPTIMAL ROBUST ESTIMATION IN SELF CALIBRATING BUNDLE ADJUSTMENT)

  • 유환희
    • 한국측량학회지
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    • 제9권1호
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    • pp.75-82
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    • 1991
  • 본 연구는 자체검정 번들조정법에서 과대오차를 처리하기 위한 최적의 Robust 추정법과 축척추정량(S.E)를 조사하는데 목적을 두고 있다. 과대오차의 검출에 있어서 여러가지 경중률을 적용하기 위하여 5가지 Robust 추정법과 3가지 축척추정량을 사용하였으며, 2가지 기준점배치형태(고밀도, 저밀도)와 3가지 과대오차(4$\sigma o$. 20$\sigma o$. 50$\sigma o$)는 비교분석을 위해 이용되었다. 그 결과, Robust 추정법중 Anscombe 추정법이 가장 좋은 정확도를 보여 주고 있으며, 기준점 배치형태에 따른 축척추정량의 적용을 분석한 결과 기준점 배치밀도가 높은 경우는 Type II 축척추정량이, 기준점 배치밀도가 낮은 경우는 Type III 축척추정량이 안정되고 정확한 결과값을 나타내었다. 따라서 정밀한 구조물 해석에 있어서 과대오차의 영향을 제거하고 정확도를 향상시킬 수 있는 최적 축척추정량을 이용한 Robust 번들조정법의 활용이 기대된다.

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확산모형에 대한 누율생성함수의 근사와 가우도 추정법 (An Approximation of the Cumulant Generating Functions of Diffusion Models and the Pseudo-likelihood Estimation Method)

  • 이윤동;이은경
    • 한국경영과학회지
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    • 제38권1호
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    • pp.201-216
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    • 2013
  • Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

Prediction of Land Use/Land Cover Change in Forest Area Using a Probability Density Function

  • Park, Jinwoo;Park, Jeongmook;Lee, Jungsoo
    • Journal of Forest and Environmental Science
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    • 제33권4호
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    • pp.305-314
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    • 2017
  • This study aimed to predict changes in forest area using a probability density function, in order to promote effective forest management in the area north of the civilian control line (known as the Minbuk area) in Korea. Time series analysis (2010 and 2016) of forest area using land cover maps and accessibility expressed by distance covariates (distance from buildings, roads, and civilian control line) was applied to a probability density function. In order to estimate the probability density function, mean and variance were calculated using three methods: area weight (AW), area rate weight (ARW), and sample area change rate weight (SRW). Forest area increases in regions with lower accessibility (i.e., greater distance) from buildings and roads, but no relationship with accessibility from the civilian control line was found. Estimation of forest area change using different distance covariates shows that SRW using distance from buildings provides the most accurate estimation, with around 0.98-fold difference from actual forest area change, and performs well in a Chi-Square test. Furthermore, estimation of forest area until 2028 using SRW and distance from buildings most closely replicates patterns of actual forest area changes, suggesting that estimation of future change could be possible using this method. The method allows investigation of the current status of land cover in the Minbuk area, as well as predictions of future changes in forest area that could be utilized in forest management planning and policymaking in the northern area.

드론 방제의 최적화를 위한 딥러닝 기반의 밀도맵 추정 (Density map estimation based on deep-learning for pest control drone optimization)

  • 성백겸;한웅철;유승화;이춘구;강영호;우현호;이헌석;이대현
    • 드라이브 ㆍ 컨트롤
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    • 제21권2호
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    • pp.53-64
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    • 2024
  • Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R2) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

A Support Vector Method for the Deconvolution Problem

  • Lee, Sung-Ho
    • Communications for Statistical Applications and Methods
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    • 제17권3호
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    • pp.451-457
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    • 2010
  • This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.