In the previous recipe, we applied a parametric estimation method. We had a statistical model (the exponential distribution) describing our data, and we estimated a single parameter (the rate of the distribution). Nonparametric estimation deals with statistical models that do not belong to a known family of distributions. The parameter space is then infinite-dimensional instead of finite-dimensional (that is, we estimate functions rather than numbers).

Here, we use a kernel density estimation (KDE) to estimate the density of probability of a spatial distribution. We look at the geographical locations of tropical cyclones from 1848 to 2013, based on data provided by the NOAA, the US' National Oceanic and Atmospheric Administration.