8.3.2022 Analysis of spatial point patterns with anomalies, covariates and intractable likelihoods (Kuronen)

Spatial point patterns arise in a number of applications from different disciplines. They represent locations of objects or events of interest. Such data is analysed and modelled using point process statistics. This work develops new statistical models and methods for challenges encountered in a few specific applications in forestry and medicine.
Mikko Kuronen
Published
8.3.2022

We consider methods for the analysis of datasets that include artefacts or missing data, introduce new point process models, and suggest tests having graphical interpretation. In one of the applications, we develop models for sweat gland activation data, which is important in early screening of diabetes. To this end, we suggest methods to handle erroneously detected points in the data produced by image analysis. We also consider modelling how the locations of tree seedlings are affected by large trees. Here we propose a Bayesian inference method for handling nonlinear covariates in a log Gaussian Cox process. Furthermore, we present an estimator for forest characteristics in data obtained by terrestrial laser scanning. The new estimator accounts for unobserved trees behind other trees. Finally, we suggest a test with a graphical interpretation for including particular covariates in a point process model.

The dissertation is published in JYU Dissertations series, number 492, Jyväskylä 2022. ISBN 978-951-39-9020-6 (PDF), ISSN 2489-9003. Link to publication:

M.Sc. Mikko Kuronen defends his doctoral dissertation in Statistics "Analysis of spatial point patterns with anomalies, covariates and intractable likelihoods" on Tuesday 8.3.2022 at 12 noon. Opponent Dr. Thordis Thorarinsdottir (Norwegian Computing Center, Norway) and Custos Professor Matti Vihola (Ä¢¹½Ö±²¥). The doctoral dissertation is held in English.