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Thomas Neyens

Researcher at University of Hasselt

Publications -  38
Citations -  246

Thomas Neyens is an academic researcher from University of Hasselt. The author has contributed to research in topics: Medicine & Overdispersion. The author has an hindex of 8, co-authored 20 publications receiving 170 citations. Previous affiliations of Thomas Neyens include Katholieke Universiteit Leuven.

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Toxicity profiles and solvent–toxicant interference in the planarian Schmidtea mediterranea after dimethylsulfoxide (DMSO) exposure

TL;DR: This study reassessed DMSO concentration limits for different experimental endpoints in the planarian S. mediterranea and proposes a statistical approach to account for solvent–toxicant interactions and discusses full‐scale solvent toxicity studies.
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Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros.

TL;DR: Analysis of two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects.
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A generalized Poisson-gamma model for spatially overdispersed data

TL;DR: A combined model is proposed: an alternative convolution model accounting for both overdispersion and spatial correlation in the data by combining the Poisson-gamma model with a spatially-structured normal CAR random effect.
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Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird

TL;DR: It is shown that landscape composition and configuration affect the connectivity between breeding (heathlands) and foraging habitats (extensively-grazed grasslands) of the European Nightjar (Caprimulgus europaeus), a crepuscular insectivorous bird.
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A zero-inflated overdispersed hierarchical Poisson model:

TL;DR: In this paper, a hierarchical Poisson model is used to model the count data in a hierarchical manner, e.g. using a hierarchical structure in the data, such as a hierarchy of columns and columns.