Publication at Faculty of Mathematics and Physics |
2014
Abstract
Many objects studied in biology, medicine or material sciences create spatial formations of random shape in which we can observe mutual interactions among those objects. In order to analyse the data composed of such patterns, we use the methods of spatial statistics.
Recently, extended random-disc Quermass-interaction process was studied, simulated and consequently statistically analysed using MCMC maximum likelihood method (MCMC MLE). However, this analysis brought some problems.
First, it was quite time-consuming, secondly, in some special cases, the parameter estimates may undervalue the real parameter values. In this paper, we describe how we can solve these problems by dimension reduction.