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- W3041382033 abstract "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 ; r ) , where r is a non-random real variable and ranges from R 1 to R 2 . 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." @default.
- W3041382033 created "2020-07-16" @default.
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- W3041382033 date "2020-07-08" @default.
- W3041382033 modified "2023-10-01" @default.
- W3041382033 title "Nonparametric Estimation of Continuously Parametrized Families of Probability Density Functions—Computational Aspects" @default.
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- W3041382033 doi "https://doi.org/10.3390/a13070164" @default.
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