Gissibl, Nadine; Klüppelberg, Claudia; Lauritzen, Steffen Identifiability and estimation of recursive max-linear models. (English) Zbl 1467.62105 Scand. J. Stat. 48, No. 1, 188-211 (2021). Summary: We address the identifiability and estimation of recursive max-linear structural equation models represented by an edge-weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAGs and edge weights corresponding to a given observational distribution. For estimation, standard likelihood theory cannot be applied because the corresponding families of distributions are not dominated. Given the underlying DAG, we present an estimator for the class of edge weights and show that it can be considered a generalized maximum likelihood estimator. In addition, we develop a simple method for identifying the structure of the DAG. With probability tending to one at an exponential rate with the number of observations, this method correctly identifies the class of DAGs and, similarly, exactly identifies the possible edge weights. Cited in 5 Documents MSC: 62H22 Probabilistic graphical models 62G32 Statistics of extreme values; tail inference 05C90 Applications of graph theory Keywords:Bayesian network; causal inference; extreme value theory; generalized maximum likelihood estimation; graphical model; structural equation model; directed acyclic graph (DAG) PDFBibTeX XMLCite \textit{N. Gissibl} et al., Scand. J. Stat. 48, No. 1, 188--211 (2021; Zbl 1467.62105) Full Text: DOI arXiv