標(biāo)題: Titlebook: Essential Wavelets for Statistical Applications and Data Analysis; R. Todd Ogden Book 1997 Springer Science+Business Media New York 1997 E [打印本頁] 作者: Addiction 時間: 2025-3-21 18:42
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書目名稱Essential Wavelets for Statistical Applications and Data Analysis被引頻次
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書目名稱Essential Wavelets for Statistical Applications and Data Analysis讀者反饋
書目名稱Essential Wavelets for Statistical Applications and Data Analysis讀者反饋學(xué)科排名
作者: 險(xiǎn)代理人 時間: 2025-3-21 20:42
Basic Smoothing Techniques,the existing techniques in use. Though there are many methods currently in use for these applications, this chapter will focus on only two of them: kernel smoothing and orthogonal series estimation. This background should provide a useful lead-in to a discussion of wavelet methods for function estim作者: 公理 時間: 2025-3-22 01:25 作者: 蛙鳴聲 時間: 2025-3-22 08:25 作者: 權(quán)宜之計(jì) 時間: 2025-3-22 12:23
Some Practical Issues,than as a powerful method for solving practical problems. In fact, much of the beauty of wavelet analysis lies in its widespread applications. This chapter will discuss important issues that arise in moving wavelets from theory to practice.作者: glowing 時間: 2025-3-22 13:36 作者: glowing 時間: 2025-3-22 20:31
Data Adaptive Wavelet Thresholding,where Section 7.1 left off, focusing on the nonparametric regression situation. The ideas described here could also be adapted for use in density estimation or other types of function estimation. To focus attention on the methods described in this chapter, it will be assumed throughout that an ortho作者: Discrete 時間: 2025-3-22 21:51 作者: analogous 時間: 2025-3-23 04:22
Essential Wavelets for Statistical Applications and Data Analysis作者: NOVA 時間: 2025-3-23 07:54
Essential Wavelets for Statistical Applications and Data Analysis978-1-4612-0709-2作者: 輕浮女 時間: 2025-3-23 10:30 作者: Gustatory 時間: 2025-3-23 15:38 作者: Rct393 時間: 2025-3-23 21:04
Book 1997ect popular (Meyer‘s book is one of the early works written with the non- specialist in mind), the implication seems to be that such an attempt some- how cheapens or coarsens the subject. I have to disagree that popularity goes hand-in-hand with debasement. is certainly a beautiful theory underlying作者: 美食家 時間: 2025-3-23 22:44 作者: figment 時間: 2025-3-24 04:22 作者: isotope 時間: 2025-3-24 08:18
Data Adaptive Wavelet Thresholding,gonal wavelet transform on the unit interval is used, and that the sample size is a power of two: . = 2. for some integer . > 0. When this condition is not met, the methods described herein may be adapted, using techniques described in Chapter 6.作者: 造反,叛亂 時間: 2025-3-24 14:15
Basic Smoothing Techniques,rnel smoothing and orthogonal series estimation. This background should provide a useful lead-in to a discussion of wavelet methods for function estimation, since standard techniques in these two areas can be modified in a straightforward manner to use wavelets.作者: 很像弓] 時間: 2025-3-24 17:33 作者: 同步左右 時間: 2025-3-24 19:10 作者: DALLY 時間: 2025-3-25 01:35 作者: JIBE 時間: 2025-3-25 04:00
sense of the word, that of making a sub- ject popular (Meyer‘s book is one of the early works written with the non- specialist in mind), the implication seems to be that such an attempt some- how cheapens or coarsens the subject. I have to disagree that popularity goes hand-in-hand with debasement.作者: Eulogy 時間: 2025-3-25 10:28 作者: 比賽用背帶 時間: 2025-3-25 15:33 作者: 成份 時間: 2025-3-25 18:33 作者: faucet 時間: 2025-3-25 21:01
Teresa J. Kennedy,Michael R. L. Odellthan as a powerful method for solving practical problems. In fact, much of the beauty of wavelet analysis lies in its widespread applications. This chapter will discuss important issues that arise in moving wavelets from theory to practice.作者: rectocele 時間: 2025-3-26 00:38
Silvia Cosimato,Nicola Cucari,Giovanni Landirm that has been the primary focus is only a small portion of all wavelet-based methods available. In this chapter, we give an overview of some of the important extensions of standard wavelet methods, and briefly consider their uses in statistics.作者: 委屈 時間: 2025-3-26 05:19
Wavelets: A Brief Introduction,s Fourier decomposition, and the wavelet representation is presented first in terms of its simplest paradigm, the Haar basis. This piecewise constant Haar system is used to describe the concepts of the multiresolution analysis, and these ideas are generalized to other types of wavelet bases.作者: Pessary 時間: 2025-3-26 09:35
Elementary Statistical Applications,cepts and examine some fundamental applications of wavelets in function estimation. This chapter will focus on wavelet versions of the two types of estimators discussed in Chapter 2 (kernels and orthogonal series), as they are applied to density estimation and nonparametric regression.作者: Ceramic 時間: 2025-3-26 14:19 作者: 是限制 時間: 2025-3-26 17:49
Generalizations and Extensions,rm that has been the primary focus is only a small portion of all wavelet-based methods available. In this chapter, we give an overview of some of the important extensions of standard wavelet methods, and briefly consider their uses in statistics.作者: cacophony 時間: 2025-3-27 00:45 作者: Coordinate 時間: 2025-3-27 04:54
Wavelet-based Diagnostics,In a real data analysis, an essential component is a thorough graphical study of the data. It is not uncommon for graphical data analysis to turn up some interesting (even vital!) aspect of the data set that might be completely overlooked by applying some canned “black box” statistical inference procedure.作者: scrutiny 時間: 2025-3-27 08:23 作者: 籠子 時間: 2025-3-27 10:47
s Fourier decomposition, and the wavelet representation is presented first in terms of its simplest paradigm, the Haar basis. This piecewise constant Haar system is used to describe the concepts of the multiresolution analysis, and these ideas are generalized to other types of wavelet bases.作者: Talkative 時間: 2025-3-27 14:41
https://doi.org/10.1057/9781137529589the existing techniques in use. Though there are many methods currently in use for these applications, this chapter will focus on only two of them: kernel smoothing and orthogonal series estimation. This background should provide a useful lead-in to a discussion of wavelet methods for function estim作者: 補(bǔ)充 時間: 2025-3-27 18:12 作者: 受傷 時間: 2025-3-28 01:13 作者: 跑過 時間: 2025-3-28 03:33 作者: 皮薩 時間: 2025-3-28 08:04
Contemporary Issues in Sustainable Financeto extend the basic methods of Chapter 3 to more sophisticated techniques on a wide variety of applications. Perhaps the most common wavelet application in statistics is nonparametric regression, which is covered in some depth in Section 7.1. This will serve as a groundwork for other applications tr作者: tendinitis 時間: 2025-3-28 13:52
Contemporary Issues in Sustainable Financewhere Section 7.1 left off, focusing on the nonparametric regression situation. The ideas described here could also be adapted for use in density estimation or other types of function estimation. To focus attention on the methods described in this chapter, it will be assumed throughout that an ortho作者: sorbitol 時間: 2025-3-28 15:35
Silvia Cosimato,Nicola Cucari,Giovanni Landirm that has been the primary focus is only a small portion of all wavelet-based methods available. In this chapter, we give an overview of some of the important extensions of standard wavelet methods, and briefly consider their uses in statistics.作者: 掙扎 時間: 2025-3-28 20:27
R. Todd OgdenIncludes supplementary material:
