Joint probability density function estimation by spectral estimate methods
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
The estimation of probability density functions (PDFs) of a given random variable (r.v.) is involved in topics related to codification, speech or whenever a short record of data is available but a greater amount is needed. Existing methods go from the so-called minimum description-length method, up to others based on the maximisation of the differential entropy imposing constraints on the moments of the r.v. In this paper we propose to estimate a PDF function by means of spectral estimate methods, since the positiveness and the real character of any PDF function allow us to deal with it as a power spectrum density function. Particularly, the minimum variance method is focused on because it can be generalised to multidimensional problems, being used in this paper to estimate the joint-PDF function of a multidimensional r.v
CitationPages, A., Lagunas, M. Joint probability density function estimation by spectral estimate methods. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "ICASSP 1996: the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing: conference proceedings: May 7-10, 1996: Marriott Marquis Hotel, Atlanta, Georgia". Atlanta, Georgia: Institute of Electrical and Electronics Engineers (IEEE), 1996, p. 2936-2939.