Analysis of Spatial Point Patterns with Anomalies, Covariates and Intractable Likelihoods
Julkaistu sarjassa
JYU dissertationsTekijät
Päivämäärä
2022Tekijänoikeudet
© The Author & University of Jyväskylä
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. 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.
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Julkaisija
Jyväskylän yliopistoISBN
978-951-39-9020-6ISSN Hae Julkaisufoorumista
2489-9003Julkaisuun sisältyy osajulkaisuja
- Artikkeli I: Kuronen, M., Henttonen, H. M., and Myllymäki, M. (2018). Correcting for nondetection in estimating forest characteristics from single-scan terrestrial laser measurements. Canadian Journal of Forest Research, 49(1), 96–103. DOI: 10.1139/cjfr-2018-0072
- Artikkeli II: Kuronen, M., Myllymäki, M., Loavenbruck, A., and Särkkä, A. (2021). Point process models for sweat gland activation observed with noise. Statistics in Medicine, 40, 2055–2072. DOI: 10.1002/sim.8891
- Artikkeli III: Kuronen, M., Särkkä, A., Vihola, M., and Myllymäki, M. (2021). Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests. Environmental and Ecological Statistics. DOI: 10.1007/s10651-021-00514-3
- Artikkeli IV: Myllymäki, M., Kuronen, M., and Mrkvicka, T. (2021). Testing global and local dependence of point patterns on covariates in parametric models. Spatial Statistics, 42, 100436. DOI: 10.1016/j.spasta.2020.100436
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