標(biāo)題: Titlebook: All of Nonparametric Statistics; Larry Wasserman Book 2006 Springer-Verlag New York 2006 Excel.Parametric statistics.STATISTICA.WholePage. [打印本頁] 作者: 無力向前 時(shí)間: 2025-3-21 17:43
書目名稱All of Nonparametric Statistics影響因子(影響力)
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書目名稱All of Nonparametric Statistics讀者反饋
書目名稱All of Nonparametric Statistics讀者反饋學(xué)科排名
作者: 壯麗的去 時(shí)間: 2025-3-21 21:24 作者: Conclave 時(shí)間: 2025-3-22 01:23 作者: 滲入 時(shí)間: 2025-3-22 05:18
Nonparametric Inference Using Orthogonal Functions,sity estimation into a Normal means problem and then we construct estimates and confidence sets using the theory from Chapter 7. In the regression case, the resulting estimators are linear smoothers and thus are a special case of the estimators described in Section 5.2. We discuss another approach t作者: ELATE 時(shí)間: 2025-3-22 08:55 作者: GRAIN 時(shí)間: 2025-3-22 16:51 作者: 紀(jì)念 時(shí)間: 2025-3-22 19:26
978-1-4419-2044-7Springer-Verlag New York 2006作者: 思考才皺眉 時(shí)間: 2025-3-22 21:16 作者: 流眼淚 時(shí)間: 2025-3-23 02:49 作者: critic 時(shí)間: 2025-3-23 07:09 作者: 誘使 時(shí)間: 2025-3-23 11:11
Abstraction and Further Reflection,Let . be a distribution with probability density . = . and let . be an . sample from .. The goal of . is to estimate . with as few assumptions about . as possible. We denote the estimator by .. As with nonparametric regression, the estimator will depend on a smoothing parameter . and choosing . carefully is important.作者: 豐滿有漂亮 時(shí)間: 2025-3-23 15:12 作者: 白楊魚 時(shí)間: 2025-3-23 19:09
Introduction,In this chapter we briefly describe the types of problems with which we will be concerned. Then we define some notation and review some basic concepts from probability theory and statistical inference.作者: auxiliary 時(shí)間: 2025-3-24 00:32
Estimating the CDF and Statistical Functionals,The first problem we consider is estimating the .. By itself, this is not a very interesting problem. However, it is the first step towards solving more important problems such as estimating statistical functionals.作者: 檢查 時(shí)間: 2025-3-24 06:00 作者: 辭職 時(shí)間: 2025-3-24 08:25 作者: 外形 時(shí)間: 2025-3-24 14:19 作者: 開玩笑 時(shí)間: 2025-3-24 16:34 作者: LANCE 時(shí)間: 2025-3-24 23:01 作者: 策略 時(shí)間: 2025-3-24 23:14 作者: exquisite 時(shí)間: 2025-3-25 04:51 作者: Ambulatory 時(shí)間: 2025-3-25 10:42
Semantic Roles in Grammatical Description,“blocks” function whose definition is given in Example 9.39. The function is very smooth except for several abrupt jumps. The top right plot shows 100 data points drawn according to .. = .(..) + .. with .. ≈ .(0, 1), and the ..s equally spaced.作者: inquisitive 時(shí)間: 2025-3-25 15:16 作者: 高度贊揚(yáng) 時(shí)間: 2025-3-25 18:07
Springer Texts in Statisticshttp://image.papertrans.cn/a/image/153419.jpg作者: 使閉塞 時(shí)間: 2025-3-25 22:38 作者: 值得 時(shí)間: 2025-3-26 02:59
Abstraction and Further Reflection, observations (.., ..), . . ., (.., ..) as in Figures 5.1, 5.2 and 5.3. The .. is related to the .. by the equations . where . is the .. The variable . is also called a .. We want to estimate (or “l(fā)earn”) the function . under weak assumptions. The estimator of .(.) is denoted by .. We also refer to 作者: adhesive 時(shí)間: 2025-3-26 07:27 作者: 不吉祥的女人 時(shí)間: 2025-3-26 11:59
Prepositions, Transparency, and Prototypes,sity estimation into a Normal means problem and then we construct estimates and confidence sets using the theory from Chapter 7. In the regression case, the resulting estimators are linear smoothers and thus are a special case of the estimators described in Section 5.2. We discuss another approach t作者: MAZE 時(shí)間: 2025-3-26 16:17 作者: clarify 時(shí)間: 2025-3-26 18:56 作者: endocardium 時(shí)間: 2025-3-26 23:27
Abstraction and Further Reflection,. is also called a .. We want to estimate (or “l(fā)earn”) the function . under weak assumptions. The estimator of .(.) is denoted by .. We also refer to . as a .. At first, we will make the simplifying assumption that the variance . does not depend on .. We will relax this assumption later.作者: 江湖騙子 時(shí)間: 2025-3-27 01:50
Prepositions, Transparency, and Prototypes,e, the resulting estimators are linear smoothers and thus are a special case of the estimators described in Section 5.2. We discuss another approach to orthogonal function regression based on wavelets in the next chapter.作者: Obstacle 時(shí)間: 2025-3-27 08:52 作者: Ingratiate 時(shí)間: 2025-3-27 10:20 作者: Fermentation 時(shí)間: 2025-3-27 13:41 作者: 蟄伏 時(shí)間: 2025-3-27 20:38 作者: Monolithic 時(shí)間: 2025-3-28 00:45
Book 2006ethods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer作者: 秘方藥 時(shí)間: 2025-3-28 05:00 作者: Conflict 時(shí)間: 2025-3-28 09:06
Book 2006 reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inferenc作者: 殺子女者 時(shí)間: 2025-3-28 10:35
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