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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Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.
Empirical measures from Kac's particle system converge to the Boltzmann equation solution for very soft potentials, proving propagation of chaos for all kernel classes.
The small-particle scaling limit of aggregate Loewner evolution from k needles is the Laplacian path model with geodesic tip growth.
An optimal transport method is proposed to construct confidence intervals with improved coverage, including theoretical consistency results, error bounds, and simulation comparisons.
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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Primitive Sequences for Probability Measures on Compact Intervals
Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.
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Propagation of chaos for the Boltzmann equation with very soft potentials
Empirical measures from Kac's particle system converge to the Boltzmann equation solution for very soft potentials, proving propagation of chaos for all kernel classes.
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Tip growth in a strongly concentrated aggregation model follows local geodesics
The small-particle scaling limit of aggregate Loewner evolution from k needles is the Laplacian path model with geodesic tip growth.
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An Optimal Transportation Approach for Improved Confidence Intervals
An optimal transport method is proposed to construct confidence intervals with improved coverage, including theoretical consistency results, error bounds, and simulation comparisons.