標(biāo)題: Titlebook: Combinatorial Methods in Density Estimation; Luc Devroye,Gábor Lugosi Book 2001 Springer-Verlag New York, Inc. 2001 Density Estimation.Lik [打印本頁] 作者: Traction 時間: 2025-3-21 17:37
書目名稱Combinatorial Methods in Density Estimation影響因子(影響力)
書目名稱Combinatorial Methods in Density Estimation影響因子(影響力)學(xué)科排名
書目名稱Combinatorial Methods in Density Estimation網(wǎng)絡(luò)公開度
書目名稱Combinatorial Methods in Density Estimation網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Combinatorial Methods in Density Estimation被引頻次
書目名稱Combinatorial Methods in Density Estimation被引頻次學(xué)科排名
書目名稱Combinatorial Methods in Density Estimation年度引用
書目名稱Combinatorial Methods in Density Estimation年度引用學(xué)科排名
書目名稱Combinatorial Methods in Density Estimation讀者反饋
書目名稱Combinatorial Methods in Density Estimation讀者反饋學(xué)科排名
作者: filial 時間: 2025-3-21 21:03 作者: 顯示 時間: 2025-3-22 01:48
Bandwidth Choice with Superkernels,l—all densities in them must be infinitely many times continuously differentiate—this is an almost pointless exercise. Instead, we consider the ultimate kernels, those for which Φ(.) = .(1/√.). Clearly, .(1/√.) is the best possible rate, as Lemma 1 shows.作者: 原告 時間: 2025-3-22 05:14 作者: Vertical 時間: 2025-3-22 10:37
H. Bauer,Ch. Korunka,M. Leodolterese densities cover ., that is, if these chosen densities are .. = {.1,…,..}, then . where .. = {. : ∫ |. ? .| ≤ .}. The smallest such .. is called the . (or .) of . (and thus is a function of .), and log... is the . of .. .. is called a . of ..作者: finale 時間: 2025-3-22 13:24 作者: finale 時間: 2025-3-22 19:20
Skeleton Estimates,ese densities cover ., that is, if these chosen densities are .. = {.1,…,..}, then . where .. = {. : ∫ |. ? .| ≤ .}. The smallest such .. is called the . (or .) of . (and thus is a function of .), and log... is the . of .. .. is called a . of ..作者: Kindle 時間: 2025-3-22 23:45 作者: 隱語 時間: 2025-3-23 01:22 作者: seduce 時間: 2025-3-23 08:52
Book 2001of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all den作者: carotenoids 時間: 2025-3-23 09:43 作者: 暗語 時間: 2025-3-23 15:27 作者: dithiolethione 時間: 2025-3-23 18:18 作者: 枯燥 時間: 2025-3-24 01:44 作者: intention 時間: 2025-3-24 06:14
Skeleton Estimates, in are totally bounded, that is, for every . > 0, there exists a finite number .. of densities in . such that the .. balls of radius . centered at these densities cover ., that is, if these chosen densities are .. = {.1,…,..}, then . where .. = {. : ∫ |. ? .| ≤ .}. The smallest such .. is called th作者: Benzodiazepines 時間: 2025-3-24 06:59
The Minimum Distance Estimate: Examples,t the skeleton estimate defined in the previous chapter always works when . is totally bounded. In this chapter we analyze the minimum distance estimate described in Section 6.8. Assume that the densities .. ∈ . are indexed by a parameter . ∈ Θ.作者: 自傳 時間: 2025-3-24 12:53 作者: 歡呼 時間: 2025-3-24 15:22
Additive Estimates and Data Splitting,o construct a density estimate .. whose .. error is (almost) as small as that of the best estimate among the .., . ∈ Θ. Applying the minimum distance estimate of Chapter 5 directly to this class is often problematic because of the dependence of each estimate in the class and the empirical measure ..作者: 松軟無力 時間: 2025-3-24 21:42
Multiparameter Kernel Estimates,almost optimal manner. The examples are all simple multiparameter versions of the kernel estimate. Once again, the methods applied here are fully combinatorial, as the only thing we need in each case is a suitable upper bound for the shatter coefficient appearing in Theorem 10.3.作者: Keratectomy 時間: 2025-3-25 02:19 作者: 專橫 時間: 2025-3-25 07:05
Bandwidth Choice with Superkernels, . an even positive integer. So, in theory, if we could find a kernel all of whose moments are vanishing, then we could hope for a rate Φ(.) that is better than .. for all ε > 0. So, is it possible to find such kernels? Can we simultaneously insure ∫ . = 1, yet ∫ ...(.) . = 0 for all . > 0? The answ作者: Haphazard 時間: 2025-3-25 11:02
Shuhei Yamaguchi,Robert T. Knightsets. As it is a function that reveals the local concentration of probability mass, it may be used to visualize distributions of random variables. The statistician’s problem, then, is to estimate . from an i.i.d. sample ..,…,.. drawn from .. A density estimate is simply a mapping .. : .. × (..). → .作者: 其他 時間: 2025-3-25 11:51
https://doi.org/10.1007/978-1-4757-1379-4le combinatorial calculations. The aim of this and the following two chapters is to equip the reader with these simple tools. We keep the material at an elementary level, with additional information added in the exercises.作者: 消散 時間: 2025-3-25 16:44 作者: Communicate 時間: 2025-3-25 23:30 作者: filial 時間: 2025-3-26 04:00 作者: 你敢命令 時間: 2025-3-26 06:00 作者: FLIT 時間: 2025-3-26 09:52
Slow Potential Changes in the Human Brainis vast, and a lot of it is ancient. The problem is that . cannot be approximated in .. by .., the empirical measure, as the total variation distance between any density . and any atomic measure (like ..) is 1. Thus, the approximation itself must have a density. The kernel estimate provides this: it作者: MIME 時間: 2025-3-26 13:33 作者: Predigest 時間: 2025-3-26 20:47 作者: Vulnerable 時間: 2025-3-27 00:26 作者: CT-angiography 時間: 2025-3-27 02:59 作者: 消散 時間: 2025-3-27 07:09
Springer Series in Statisticshttp://image.papertrans.cn/c/image/229941.jpg作者: 合唱團(tuán) 時間: 2025-3-27 13:24
Oscar Herreras,George G. SomjenThis chapter is devoted to some basic inequalities that bound the maximal difference between probabilities and relative frequencies over a class of events. The bounds will be key tools in our study of density estimates. Let ..,…,.. be i.i.d. random variables taking values in .. with common distribution 作者: 徹底明白 時間: 2025-3-27 17:19
https://doi.org/10.1007/978-1-4899-1597-9Consider a class . of subsets of .., and let ..,…,.. ∈ .. be arbitrary points. Recall from the previous chapter that properties of the finite set .(..) ? {0, 1}. defined by . play an essential role in bounding uniform deviations of the empirical measure.作者: 落葉劑 時間: 2025-3-27 21:30
https://doi.org/10.1007/3-7643-7537-XThis chapter is about the choice of the bandwidth (or smoothing factor) . ∈ (0, ∞) of the standard kernel estimate 作者: MIRTH 時間: 2025-3-27 23:08 作者: 壯麗的去 時間: 2025-3-28 04:57
https://doi.org/10.1007/3-7643-7537-XThe transformed kernel estimate on the real line was introduced in an attempt to reduce the .. error in a relatively cheap manner. The data are first transformed . : . → . by a strictly monotonically increasing almost everywhere differentiable transformation .: .. = .(..),…,.. = .(..). The density of .. is . where .. denotes the inverse of ..作者: hypnotic 時間: 2025-3-28 08:18 作者: engrave 時間: 2025-3-28 13:44
Uniform Deviation Inequalities,This chapter is devoted to some basic inequalities that bound the maximal difference between probabilities and relative frequencies over a class of events. The bounds will be key tools in our study of density estimates. Let ..,…,.. be i.i.d. random variables taking values in .. with common distribution 作者: Lacerate 時間: 2025-3-28 18:35
Combinatorial Tools,Consider a class . of subsets of .., and let ..,…,.. ∈ .. be arbitrary points. Recall from the previous chapter that properties of the finite set .(..) ? {0, 1}. defined by . play an essential role in bounding uniform deviations of the empirical measure.作者: Interregnum 時間: 2025-3-28 21:43 作者: Endoscope 時間: 2025-3-29 01:18 作者: synchronous 時間: 2025-3-29 06:59
The Transformed Kernel Estimate,The transformed kernel estimate on the real line was introduced in an attempt to reduce the .. error in a relatively cheap manner. The data are first transformed . : . → . by a strictly monotonically increasing almost everywhere differentiable transformation .: .. = .(..),…,.. = .(..). The density of .. is . where .. denotes the inverse of ..作者: Throttle 時間: 2025-3-29 07:25 作者: 確定 時間: 2025-3-29 11:49 作者: definition 時間: 2025-3-29 17:36
https://doi.org/10.1007/978-1-4757-1379-4le combinatorial calculations. The aim of this and the following two chapters is to equip the reader with these simple tools. We keep the material at an elementary level, with additional information added in the exercises.作者: 恫嚇 時間: 2025-3-29 22:37
Slow Potential Changes in the Human Brain). More precisely, given the sample .., …, .. distributed according to density ., we are asked to construct a density estimate .. such that . This simple problem turns out to be surprisingly difficult, even if the estimates .. and .. are fixed densities, not depending on the data.作者: Vulnerary 時間: 2025-3-30 02:24 作者: enmesh 時間: 2025-3-30 08:05
S. E. G. Nilsson,O. Textorius,E. Welindero construct a density estimate .. whose .. error is (almost) as small as that of the best estimate among the .., . ∈ Θ. Applying the minimum distance estimate of Chapter 5 directly to this class is often problematic because of the dependence of each estimate in the class and the empirical measure ...作者: A精確的 時間: 2025-3-30 10:09 作者: FLEET 時間: 2025-3-30 14:16
https://doi.org/10.1007/3-7643-7537-Xlass ., which is given to us. Our estimate of . is .(.), where . should be such that it is best possible. As we do not know ., we naturally assume that after . is picked, our adversary picks the worst . ? ..作者: Discrete 時間: 2025-3-30 20:15 作者: 新奇 時間: 2025-3-30 21:38 作者: Myocarditis 時間: 2025-3-31 04:17 作者: 排出 時間: 2025-3-31 07:22
Additive Estimates and Data Splitting,o construct a density estimate .. whose .. error is (almost) as small as that of the best estimate among the .., . ∈ Θ. Applying the minimum distance estimate of Chapter 5 directly to this class is often problematic because of the dependence of each estimate in the class and the empirical measure ...
