Estimation of the K-function of a point process using global normalization

Point processes are random local finite sets of points in a space that are used for mod- elling and subsequent spatial data analysis. Same of their useful characteristics are the pair correlation function and also the K-function, which describe point interactions with respect to the distance between points. There are several ways to include informa- tion about the non-constant intensity function in the estimates of these characteristics for inhomogeneous processes. In the older estimate, we use information about a value of the intensity function only in places where the process points are located. However, the new estimate works with a value of the intensity function within the whole observation window. In this thesis we focus on the comparison of these two estimates. In the third chapter we theoretically present these estimates and in the fourth chapter we compare their behaviour based on simulations of 8 point process models, while finding the optimal value of bandwidth for their kernel estimates.