Zero-inflated
models are usually applied to data that contains an excess of zeroes compared
to what a classical model can explain (log-normal, Poisson, negative binomial).
To counter the problem, separate models are set up for the probability of
obtaining a positive measurement, and for the positive measurements themselves.

## Requirements

Knowledge on Likelihood and statistical modelling

The typical situation is that the model for the positive numbers allows for either zeroes as natural model results, or values below a detection limit, but other observations can be zero for completely different reasons. A standard example is contamination of fish. Some fish may have a low concentration of a contaminant, below a detection limit, while other fish may have been living in areas where the contaminant was not present.

However, zero-inflated models often lead to biased results. There is no clear method to handle bias in zero-inflated models. The project consists of characterizing the nature of bias in a zero-inflated model through likelihood approaches, characterize the level of bias from approaches where a standard model is applied to the positive values (ie. a standard lognormal, Poisson, or negative binomial), rather than a conditional model. If the project time allows, solutions can be attempted to be developed.
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For almost two centuries DTU, Technical University of Denmark, has been dedicated to fulfilling the vision of H.C. Ørsted – the father of electromagnetism – who founded the university in 1829 to develop and create value using the natural sciences and the technical sciences to benefit society.

Today, DTU is ranked as one of the foremost technical universities in Europe, continues to set new records in the number of publications, and persistently increases and develops our partnerships with industry, and assignments accomplished by DTU’s public sector consultancy.

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Denmark

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CVR-nr. 30 06 09 46

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