Open AccessEstimating the distribution density function using a DOG wavelet
Author(s) -
В. С. Тимофеев,
Elena Isaeva
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1661/1/012084
Subject(s) - wavelet , orthogonality , mathematics , estimator , probability density function , smoothing , random variable , statistics , cascade algorithm , wavelet transform , algorithm , discrete wavelet transform , computer science , artificial intelligence , geometry
It is often necessary to obtain information on the distribution form of a random variable within the framework of statistical data analysis. There are several ways of estimating probability density distributions. Wavelet density estimators distribution of a random variable, built with DOG wavelet, would be considered. Three methods are proposed for determining the normalizing parameter, which provides a correction for the non-orthogonality of the DOG wavelet. In addition, studies have been carried out on the impact on quality wavelet estimates of parameters such as sample size, a number of coefficients of expansion, a function by series in the expression for wavelet density estimates. In the course of this process, it was noted that the quality of estimates on the smoothing parameter is substantial. Moreover, there is its best meaning. The conclusion is made that the estimate is based on DOG wavelet, provides quality recovery density functions.