Joint probability density function estimation by spectral estimate methods
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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.