Nonparametric density estimation via wavelets
Abstract
A new methodology for the application of wavelets in non-parametric density estimation is proposed. It transfers a density estimation problem into a regression problem by binning the observations, and then treating the square root of the observation counts as the new data for regression. It then uses a wavelet regression method to recover the square root of the density. Because of the automatic adaptivity of wavelet methods, this density estimation method achieves the optimal convergence rate and is computationally efficient. Data from the call service center of a large Northeastern bank is used to demonstrate the usage of this method for practical problems. In this setting the density estimator is used to describe the arrival rate of phone calls as a function of covariates such as time-of-day and day of the week.
Recommended Citation
Ren Zhang,
"Nonparametric density estimation via wavelets"
(January 1, 2002).
Dissertations available from ProQuest.
Paper AAI3043983.
http://repository.upenn.edu/dissertations/AAI3043983
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