Infill asymptotics and bandwidth selection for kernel estimators of spatial intensity functions
We investigate the asymptotic mean squared error of kernel estimators of the intensity function of a spatial point process. We derive expansions for the bias and variance in the scenario that n independent copies of a point process in Rd are superposed. When the same bandwidth is used in all d dimensions, we show that an optimal bandwidth exists and is of the order n−1/(d+4) under appropriate smoothness conditions on the true intensity function.
|Keywords||Bandwidth, Infill asymptotics, Intensity function, Kernel estimator, Mean squared error, Point process|
|Journal||Methodology and Computing in Applied Probability|
van Lieshout, M.N.M. (2019). Infill asymptotics and bandwidth selection for kernel estimators of spatial intensity functions. Methodology and Computing in Applied Probability, 1–14. doi:10.1007/s11009-019-09749-x