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- W2976685687 abstract "A recursive max-linear model is a structural equation model in which the dependence structure between the random variables is represented by a directed acyclic graph. In comparison to usual Gaussian structural equation models sums are replaced by maxima and the Gaussian distribution is replaced by the standard Frechet-distribution. Hence, well-known estimation methods that uses conditional independence to infer the structure of the underlying unknown DAG cannot be applied anymore. In this thesis, we develop a new Branch & Bound algorithm to estimate the topological order of the nodes of a recursive max-linear model with underlying unknown directed acyclic graph. We extend the recursive max-linear model and introduce multiplicative noise in two different ways, first in a recursive manner and then as Hadamard product in order to test the new algorithm also in situations that come close to real world scenarios. A simulation study shows that the new algorithm performs very well, if we have non-noisy observations as well as if we have noisy observations." @default.
- W2976685687 created "2019-10-03" @default.
- W2976685687 creator A5032062177 @default.
- W2976685687 date "2019-01-01" @default.
- W2976685687 modified "2023-09-27" @default.
- W2976685687 title "Identifiability and Estimation of Recursive Max-Linear Models" @default.
- W2976685687 hasPublicationYear "2019" @default.
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