Let a class $\F$ of densities be given. We draw an i.i.d.\ sample from a density $f$ which may or may not be in $\F$. After every $n$, one must make a guess whether $f \in \F$ or not. A class is almost surely testable if there exists such a testing sequence such that for any $f$, we make...

A local likelihood density estimator is shown to have asymptotic bias depending on the dimension of the local parameterization. Comparing with kernel estimation it is demonstrated using a variety of bandwidths that we may obtain as good and potentially even better estimates using local...

Local polynomial fitting for univariate data has been widely studied and discussed, but up until now the multivariate equivalent has often been deemed impractical, due to the so-called curse of dimensionality. Here, rather than discounting it completely, we use density as a threshold to...