Paper Title:

Haar Wavelets in Data Analysis

Periodical Advanced Materials Research (Volumes 121 - 122)
Main Theme Nanotechnology and Computer Engineering
Edited by Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
Pages 346-349
DOI 10.4028/www.scientific.net/AMR.121-122.346
Citation Yu Qin Sun et al., 2010, Advanced Materials Research, 121-122, 346
Online since June, 2010
Authors Yu Qin Sun, Yuan Ttao Jiang, Yong Ge Tian
Keywords Data Analysis, Estimation, Haar Wavelets, Regression Model
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Abstract

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.