Fraction-of- Time Density Estimation Based on Linear Interpolation of Time Series

A new estimator for the probability density function of a signal observed over a finite observation interval is proposed. The estimator linearly interpolates adjacent samples and accommodates the presence of probability masses. The analysis is carried out in the fraction-of-time (FOT) probability fr...

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Bibliographic Details
Published in:2021 Systems of Signals Generating and Processing in the Field of on Board Communications pp. 1 - 4
Main Authors: Shevgunov, Timofey, Napolitano, Antonio
Format: Conference Proceeding
Language:English
Published: IEEE 16.03.2021
Subjects:
Analytical models
cyclostationary processes
Density measurement
Estimation
fraction-of-time probability
Interpolation
probability density estimator
Probability density function
Stochastic processes
Time series analysis
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Description
Summary:A new estimator for the probability density function of a signal observed over a finite observation interval is proposed. The estimator linearly interpolates adjacent samples and accommodates the presence of probability masses. The analysis is carried out in the fraction-of-time (FOT) probability framework where signals are modeled as single functions of time rather than sample paths of a stochastic process. Numerical results show the better performance of the proposed estimator with respect to the kernel-based estimator. Moreover, the usefulness of analyzing signals in the FOT framework is enlightened.
DOI:10.1109/IEEECONF51389.2021.9415991