Abstract
In data analysis, we must be conscious of the probability density function of population distribution. Then it is a problem why the probability density function is expressed.
The estimation of a probability density function based on a sample of independent identically distributed observations is essential in a wide range of applications. The estimation method of probability density function – (1)a parametric method (2)a nonparametric method and (3)a semi-parametric method etc. – it is. In this paper, GMM problem is taken up as a semi-parametric method and We use a wavelet method as a powerful new technique. Compactly supported wavelets are particularly interesting because of their natural ability to represent data with intrinsically local properties.
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© 2013 Springer International Publishing Switzerland
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Tsukagoshi, K., Ida, K., Yokota, T. (2013). The GMM Problem as One of the Estimation Methods of a Probability Density Function. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds) Active Media Technology. AMT 2013. Lecture Notes in Computer Science, vol 8210. Springer, Cham. https://doi.org/10.1007/978-3-319-02750-0_39
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DOI: https://doi.org/10.1007/978-3-319-02750-0_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02749-4
Online ISBN: 978-3-319-02750-0
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