10 December 2014 13:00 in CM105
Under many circumstances the application of classical (generalized) linear regression is not enough to describe
the relationship between a set of covariates and a dependent variable. Especially the key assumption of a
closed form distribution is violated frequently. One of the approaches to overcome those problems is quantile
regression, developed by Roger Koenker in the 1970s. Even though quantile regression is widely used by now,
there is no standard approach for modelling the impact of covariates on two or more dependent variables
simultaneously. Our developments are motivated by the analysis of data from the field of biodiversity, where
we want to use covariates, like temperature, topographic diversity (the maximal elevational range within one
region), habitatial diversity (the abundance of different ecosystems in one region) and the number of rainy
days to explain both, the number of animal species and plant species in one region.
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See the Stats4Grads page for more details about this series.