Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers
نویسندگان
چکیده
Directed acyclic graph (DAG) models are widely used to represent casual relationships among random variables in many application domains. This article studies a special class of non-Gaussian DAG models, where the conditional variance each node given its parents is quadratic function mean. Such fairly flexible and admit popular distributions as cases, including Poisson, Binomial, Geometric, Exponential, Gamma. To facilitate learning, we introduce novel concept topological layers, develop an efficient learning algorithm. It first reconstructs layers hierarchical fashion then recovers directed edges between nodes different which requires much less computational cost than most existing algorithms literature. Its advantage also demonstrated number simulated examples, well applications two real-life datasets, NBA player statistics data cosmetic sales collected by Alibaba. Supplementary materials for this available online.
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2022
ISSN: ['1061-8600', '1537-2715']
DOI: https://doi.org/10.1080/10618600.2022.2069776