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在“Elsevier电子期刊”中，命中：812条，耗时：0.0519752 秒

在所有数据库中总计命中：6,506条

1.Density estimation on manifolds with boundary

作者：Tyrus Berry ; ^{tberry@gmu.edu} ; Timothy Sauer ^{tsauer@gmu.edu}

关键词：Kernel density estimation ; Manifold learning ; Boundary correction ; Geometric prior

刊名：Computational Statistics & Data Analysis

出版年：2017

2.In search of an optimal kernel for a bias correction method for density estimators

作者：Lyudmila Sakhanenko ^{luda@stt.msu.edu}

关键词：Kernel smoothing ; Integrated mean square error ; Density estimation

刊名：Statistics & Probability Letters

出版年：2017

3.Dimension reduction estimation for probability density with data missing at random when covariables are present

作者：Jianqiu Deng ; Qihua Wang ; ^{qhwang@amss.ac.cn" class="auth_mail" title="E-mail the corresponding author}

关键词：Kernel density estimation ; Kernel regression ; Dimension reduction ; Missing at random ; Asymptotic normality

刊名：Journal of Statistical Planning and Inference

出版年：2017

4.Comparative study of species sensitivity distributions based on non-parametric kernel density estimation for some transition metals

作者：Ying Wang^a ; Chenglian Feng^a ; ^{fengchenglian@163.com} ; Yuedan Liu^b ; Yujie Zhao^a ; Huixian Li^a ; Tianhui Zhao^a ; Wenjing Guo^a ; ^c

关键词：Probabilistic ; SSD ; Transition metals ; HC5 ; Water quality criteria

刊名：Environmental Pollution

出版年：2017

5.MILKDE: A new approach for multiple instance learning based on positive instance selection and kernel density estimation

作者：A.W.C. Faria^a ; F.G.F. Coelho^a ; A.M. Silva^b ; H.P. Rocha^a ; G.M. Almeida^c ; A.P. Lemos^a ; A.P. Braga^a ;

关键词：Multiple instance learning ; KDE ; Instance selection ; Instance ranking

刊名：Engineering Applications of Artificial Intelligence

出版年：2017

6.Reducing bias in nonparametric density estimation via bandwidth dependent kernels: view

作者：Kairat Mynbaev^a ; ^{kairat_mynbayev@yahoo.com} ; Carlos Martins-Filho^b ; ^c ; ^{carlos.martins@colorado.edu} ; ^{c.martins-filho@cgiar.org}

关键词：62G07 ; 62G10 ; 62G20

刊名：Statistics & Probability Letters

出版年：2017

7.Synchronization clustering based on central force optimization and its extension for large-scale datasets

作者：Wenlong Hang^a ; ^b ; ^{hwl881018@163.com} ; Kup-Sze Choi^b ; ^{kschoi@ieee.org} ; Shitong Wang^a ; ^c ; ^{wxwangst@aliyun.com}

关键词：Gravitational kinematics ; Central force optimization ; Partial synchronization ; Synchronization clustering ; Fast kernel density estimation ; Large-scale datasets

刊名：Knowledge-Based Systems

出版年：2017

8.Effects of canopy gap size on growth and spatial patterns of Chinese pine (Pinus tabulaeformis) regeneration

作者：Zhibin Wang^a ; ¹ ; Haijiao Yang^c ; ¹ ; Boqian Dong^a ; Mingming Zhou^a ; Lvyi Ma^a ; ^b ; ^{maluyi@bjfu.edu.cn} ; Zhongkui Jia^a ; ^b ; ^{zhibwang@outlook.com} ; Jie Duan^b

关键词：all Chinese pine regeneration in each gap ; which was the sum of REBG and REAG ; ALL ; the Chinese pine regeneration established before gap creation ; REBG ; the Chinese pine regeneration established after gap creation ; REAG ; average canopy tree height ; H ; the crown radius of each gap border tree projecting toward the gap center ; CR1 ; the crown radius of each gap border tree projecting toward the closed canopy ; CR2 ; kernel density estimation ; KDE

刊名：Forest Ecology and Management

出版年：2017

9.Harmonic modelling and prediction of high speed electric train based on non-parametric confidence interval estimation method

作者：Minwu Chen^a ; ^{chenminwu@home.swjtu.edu.cn} ; Clive Roberts^b ; Paul Weston^b ; Stuart Hillmansen^b ; Ning Zhao^b ; Xudong Han^a

关键词：Electric train ; Harmonic modelling ; Prediction ; Non-parametric estimation ; Confidence interval

刊名：International Journal of Electrical Power & Energy Systems

出版年：2017

10.Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing

作者：Wenbo Mo^a ; Yong Wang^b ; ^{wangy@igsnrr.ac.cn" class="auth_mail" title="E-mail the corresponding author} ; Yingxue Zhang^a ; Dafang Zhuang^b

关键词：Road network ; Landscape ecological risk ; Kernel density spatial analysis ; Beijing

刊名：Science of the Total Environment

出版年：2017

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