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: Wavelets and Statistics; Anestis Antoniadis,Georges Oppenheim Book 1995 Springer-Verlag New York 1995 Gaussian process.Hypothese.Markov ra

[復(fù)制鏈接]
61#
發(fā)表于 2025-4-1 05:53:37 | 只看該作者
Discretized Wavelet Density Estimators for Continuous Time Stochastic Processes,ch are satisfied for rather general diffusion processes, the. . error of the linear wavelet estimator of. constructed from the observation . converges with the rate . when . In this work we study two discretized versions of this estimator, constructed from the dicrete observations . We show that the
62#
發(fā)表于 2025-4-1 07:24:47 | 只看該作者
63#
發(fā)表于 2025-4-1 10:47:15 | 只看該作者
64#
發(fā)表于 2025-4-1 16:51:50 | 只看該作者
65#
發(fā)表于 2025-4-1 20:57:01 | 只看該作者
66#
發(fā)表于 2025-4-2 02:42:19 | 只看該作者
Choice of the Threshold Parameter in Wavelet Function Estimation, data. The choice of threshold is crucial to the success of the method and is currently subject to an intense research effort. We describe how we have applied the statistical technique of cross-validation to choose a threshold and we present results that indicate that its performance for correlated
67#
發(fā)表于 2025-4-2 03:29:02 | 只看該作者
Choice of the Threshold Parameter in Wavelet Function Estimation, data. The choice of threshold is crucial to the success of the method and is currently subject to an intense research effort. We describe how we have applied the statistical technique of cross-validation to choose a threshold and we present results that indicate that its performance for correlated
68#
發(fā)表于 2025-4-2 08:23:43 | 只看該作者
The Stationary Wavelet Transform and some Statistical Applications,useful subsequently in the paper. A ‘stationary wavelet transform’, where the coefficient sequences are not decimated at each stage, is described. Two different approaches to the construction of an inverse of the stationary wavelet transform are set out. The application of the stationary wavelet tra
69#
發(fā)表于 2025-4-2 11:57:17 | 只看該作者
70#
發(fā)表于 2025-4-2 18:05:01 | 只看該作者
Wavelet Thresholding: Beyond the Gaussian I.I.D. Situation, of these applications are based on. of the empirical coefficients. For regression and density estimation with independent observations, we establish joint asymptotic normality of the empirical coefficients by means of strong approximations. Then we describe how one can prove asymptotic normality un
 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(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-11 21:45
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
新建县| 晋江市| 通河县| 密山市| 大同市| 那坡县| 庄浪县| 绥芬河市| 库伦旗| 芮城县| 维西| 台湾省| 永宁县| 武隆县| 宽城| 越西县| 漳平市| 育儿| 德州市| 湟源县| 梁山县| 布尔津县| 师宗县| 新余市| 定边县| 木兰县| 来凤县| 鸡西市| 钦州市| 绥芬河市| 峨眉山市| 乌拉特中旗| 新疆| 始兴县| 屯门区| 明光市| 沅陵县| 怀柔区| 台州市| 大渡口区| 永济市|