Develops clr-based local indicators of mark association for composition-valued marks in spatial point processes to detect local heterogeneity invisible to global metrics.
Annual Review of Statistics and Its Application 8, 271–299
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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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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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.