NDLI: Nonparametric Estimation of Continuously Parametrized Families of Probability Density Functions

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Nonparametric Estimation of Continuously Parametrized Families of Probability Density Functions—Computational Aspects

Content Provider	MDPI
Author	Wojciech, Rafajłowicz
Copyright Year	2020
Description	We consider a rather general problem of nonparametric estimation of an uncountable set of probability density functions (p.d.f.’s) of the form: $f(x;$ , where r is a non-random real variable and ranges from $R1$ to $R2$ . We put emphasis on the algorithmic aspects of this problem, since they are crucial for exploratory analysis of big data that are needed for the estimation. A specialized learning algorithm, based on the 2D FFT, is proposed and tested on observations that allow for estimate p.d.f.’s of a jet engine temperatures as a function of its rotation speed. We also derive theoretical results concerning the convergence of the estimation procedure that contains hints on selecting parameters of the estimation algorithm.
Starting Page	164
e-ISSN	19994893
DOI	10.3390/a13070164
Journal	Algorithms
Issue Number	7
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2020-07-08
Access Restriction	Open
Subject Keyword	Algorithms Statistics and Probability Nonparametric Estimation Fft Family of Probability Density Functions
Content Type	Text
Resource Type	Article

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