A0585
Title: Nonparametric collective (spectral) density estimation with applications in Bioinformatics
Authors: Mehdi Maadooliat - Marquette University (United States) [presenting]
Abstract: A nonparametric method is reviewed for the collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. An extension of this approach is then presented for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model, and an alternating block-wise Newton-type algorithm is developed for the computation. A web-based ShinyApp is developed for visualization, training, and learning the SDFs collectively using the proposed technique. Finally, the method is applied to cluster similar brain signals recorded by the electroencephalogram to identify synchronized brain regions according to their spectral densities.
