Conference PaperThis paper introduces a new multiscale method for nonparametric piecewise polynomial intensity and density estimation of point processes. Fast, piecewise polynomial, maximum penalized likelihood methods for intensity and density estimation are developed. The recursive partitioning scheme underlying these methods is based on multiscale likelihood factorizations which, unlike conventional wavelet decompositions, are very well suited to applications with point process data. Experimental results demonstrate that multiscale methods can outperform wavelet and kernel based density estimation methods.Office of Naval ResearchArmy Research OfficeNational Science Foundatio
Wavelet theory constitutes one of the most significant mathematical advances for signal processing, ...
To automatically identify arbitrarily-shaped clusters in point data, a theory of point process decom...
Point processes describe random point patterns in space. One of their most important characteristics...
Tech ReportThe nonparametric density estimation method proposed in this paper is computationally fas...
The nonparametric multiscale polynomial and platelet algorithms presented in this thesis are powerfu...
Masters ThesisThe nonparametric multiscale polynomial and platelet algorithms presented in this thes...
Conference PaperThe nonparametric multiscale polynomial and platelet methods presented here are powe...
Elec 599 Project ReportGiven observations of a one-dimensional piecewise linear, length-M Poisson in...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
Journal PaperWe describe here a framework for a certain class of multiscale likelihood factorization...
Abstract In this article we consider the problem of estimating the intensity of a non-homogeneous po...
International audienceThis paper deals with feature selection procedures for spatial point processes...
The paper introduces a framework for non-linear multiscale decompositions of Poisson data that have ...
We take a wavelet based approach to the analysis of point processes and the estimation of the first ...
In this paper we discuss some preliminary results related to a novel Bayesian nonparametric method ...
Wavelet theory constitutes one of the most significant mathematical advances for signal processing, ...
To automatically identify arbitrarily-shaped clusters in point data, a theory of point process decom...
Point processes describe random point patterns in space. One of their most important characteristics...
Tech ReportThe nonparametric density estimation method proposed in this paper is computationally fas...
The nonparametric multiscale polynomial and platelet algorithms presented in this thesis are powerfu...
Masters ThesisThe nonparametric multiscale polynomial and platelet algorithms presented in this thes...
Conference PaperThe nonparametric multiscale polynomial and platelet methods presented here are powe...
Elec 599 Project ReportGiven observations of a one-dimensional piecewise linear, length-M Poisson in...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
Journal PaperWe describe here a framework for a certain class of multiscale likelihood factorization...
Abstract In this article we consider the problem of estimating the intensity of a non-homogeneous po...
International audienceThis paper deals with feature selection procedures for spatial point processes...
The paper introduces a framework for non-linear multiscale decompositions of Poisson data that have ...
We take a wavelet based approach to the analysis of point processes and the estimation of the first ...
In this paper we discuss some preliminary results related to a novel Bayesian nonparametric method ...
Wavelet theory constitutes one of the most significant mathematical advances for signal processing, ...
To automatically identify arbitrarily-shaped clusters in point data, a theory of point process decom...
Point processes describe random point patterns in space. One of their most important characteristics...