找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Lectures on the Nearest Neighbor Method; Gérard Biau,Luc Devroye Book 2015 Springer International Publishing Switzerland 2015 Density Esti

[復(fù)制鏈接]
樓主: STRI
11#
發(fā)表于 2025-3-23 11:33:03 | 只看該作者
The ,-nearest neighbor density estimatef . is a small ball about ., the probability that . falls in . is about .(.) times the volume of .. It thus serves as a tool for computing probabilities of sets and, as a function that reveals the local concentration of probability mass, it may be used to visualize distributions of random variables.
12#
發(fā)表于 2025-3-23 14:47:17 | 只看該作者
13#
發(fā)表于 2025-3-23 19:20:05 | 只看該作者
The nearest neighbor regression function estimate . depend on the values of the observation vector .. The objective is to find a Borel measurable function . such that?|?. ? .(.)?|?is small, where “small” could be defined in terms of the .. risk . (.?>?0), for example. Of particular interest is the .. risk of .,
14#
發(fā)表于 2025-3-23 23:25:34 | 只看該作者
The 1-nearest neighbor regression function estimatewill also offer the opportunity to familiarize the reader with concepts that will be encountered in the next few chapters. Recall that this very simple estimation procedure is defined by setting . where . is a reordering of the data according to increasing values of ., and distance ties are broken by looking at indices.
15#
發(fā)表于 2025-3-24 05:27:49 | 只看該作者
Regression: the noiseless case. errors with respect to the Lebesgue measure on compacts. Others use it for Monte Carlo purposes, wanting to estimate . over a compact set .. The model we study here takes a sample . of i.i.d.?random vectors with a density . on . that is not known.
16#
發(fā)表于 2025-3-24 10:07:51 | 只看該作者
Gérard Biau,Luc DevroyePresents a rigorous overview of nearest neighbor methods.Many different components covered: statistical, probabilistic, combinatorial, and geometric ideas.Extensive appendix material provided
17#
發(fā)表于 2025-3-24 14:15:40 | 只看該作者
18#
發(fā)表于 2025-3-24 15:39:30 | 只看該作者
Order statistics and nearest neighborsWe start with some basic properties of uniform order statistics. For a general introduction to probability, see Grimmett and Stirzaker (2001). Some of the properties of order statistics presented in this chapter are covered by Rényi (1970); Galambos (1978), and Devroye (1986).
19#
發(fā)表于 2025-3-24 22:13:55 | 只看該作者
20#
發(fā)表于 2025-3-25 02:07:45 | 只看該作者
Uniform consistencyThis chapter is devoted to the study of the uniform consistency properties of the .-nearest neighbor density estimate ... Before embarking on the supremum norm convergence, it is useful to understand the behavior of .. on bounded densities. We denote the essential supremum (with respect to the Lebesgue measure .) of the density . by
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-12 21:00
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
丹棱县| 宜阳县| 邻水| 西峡县| 东丰县| 蓝山县| 天等县| 古丈县| 建德市| 商河县| 龙井市| 灵武市| 黄大仙区| 丹棱县| 青海省| 龙岩市| 同心县| 长子县| 隆回县| 高清| 巢湖市| 师宗县| 石楼县| 黄冈市| 永靖县| 利辛县| 宜昌市| 福贡县| 肃宁县| 浙江省| 平乐县| 麻栗坡县| 万荣县| 铁力市| 长乐市| 廉江市| 洱源县| 泽普县| 阿克苏市| 安达市| 吴江市|