Develops clr-based local indicators of mark association for composition-valued marks in spatial point processes to detect local heterogeneity invisible to global metrics.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
BDARMA models applied to platform booking data forecast tourist origin market shares with 27% lower error than naive methods for EMEA regions while respecting the unit-sum constraint.
citing papers explorer
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Uncovering Local Heterogeneity: Local Summary Characteristics for Spatial Point Processes with Composition-Valued Marks
Develops clr-based local indicators of mark association for composition-valued marks in spatial point processes to detect local heterogeneity invisible to global metrics.
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Bayesian Modeling and Prediction of Generalized Contact Matrices
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
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Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data
BDARMA models applied to platform booking data forecast tourist origin market shares with 27% lower error than naive methods for EMEA regions while respecting the unit-sum constraint.
- Aitchison Embeddings for Learning Compositional Graph Representations