  Accurate density estimation methodologies play an integral role in a variety
of scientific disciplines, with applications including simulation models,
decision support tools, and exploratory data analysis. In the past, histograms
and kernel density estimators have been the predominant tools of choice,
primarily due to their ease of use and mathematical simplicity. More recently,
the use of wavelets for density estimation has gained in popularity due to
their ability to approximate a large class of functions, including those with
localized, abrupt variations. However, a well-known attribute of wavelet bases
is that they can not be simultaneously symmetric, orthogonal, and compactly
supported. Multiwavelets-a more general, vector-valued, construction of
wavelets-overcome this disadvantage, making them natural choices for estimating
density functions, many of which exhibit local symmetries around features such
as a mode. We extend the methodology of wavelet density estimation to use
multiwavelet bases and illustrate several empirical results where multiwavelet
estimators outperform their wavelet counterparts at coarser resolution levels.
