M
Mari Myllymäki
Researcher at Natural Resources Institute Finland
Publications - 50
Citations - 775
Mari Myllymäki is an academic researcher from Natural Resources Institute Finland. The author has contributed to research in topics: Point process & Test statistic. The author has an hindex of 14, co-authored 45 publications receiving 646 citations. Previous affiliations of Mari Myllymäki include University of Jyväskylä & Aalto University.
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Journal ArticleDOI
Global envelope tests for spatial processes
TL;DR: In this paper, two approaches related to Barnard's Monte Carlo test are proposed for building global envelope tests on I: ordering the empirical and simulated functions on the basis of their r-wise ranks among each other and the construction of envelopes for a deviation test.
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Correct testing of mark independence for marked point patterns
TL;DR: In this article, the use of deviation tests for testing hypotheses of mark independence is discussed in detail, and the envelope test can be refined so that it becomes a valuable tool both for statistical inference and for understanding the reasons of possible rejections of the independence hypothesis.
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Second-order spatial analysis of epidermal nerve fibers.
Lance A. Waller,Aila Särkkä,Aila Särkkä,Viktor Olsbo,Viktor Olsbo,Mari Myllymäki,Ioanna G. Panoutsopoulou,William R. Kennedy,Gwen Wendelschafer-Crabb +8 more
TL;DR: It appears that the spatial distribution of nerve fibers becomes more ‘clustered’ as neuropathy advances, suggesting the possibility of diagnostic prediction based on patterns observed in skin biopsies.
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Multiple Monte Carlo testing, with applications in spatial point processes
TL;DR: The rank envelope test (Myllymäki et al. in J R Stat Soc B, 2016) is proposed as a solution to the multiple testing problem for Monte Carlo tests and a power comparison to the classical multiple testing procedures is given.
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Model-assisted forest inventory with parametric, semiparametric, and nonparametric models
TL;DR: In this article, the authors analyzed how well the difference estimator works for different types of models, both internal and external, in simulated populations produced using a C-vine copula model with empirical marginals.