A data-based geometric quantile loss on Hadamard spaces is defined and shown to improve on parameter-based quantiles in consistency, asymptotic normality, robustness, extreme behavior, and computability.
Quantiles on global non-positive curvature spaces
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abstract
This paper develops a notion of geometric quantiles on Hadamard spaces, also known as global non-positive curvature spaces. After providing some definitions and basic properties, including scaled isometry equivariance and a necessary condition on the gradient of the quantile loss function at quantiles on Hadamard manifolds, we investigate asymptotic properties of sample quantiles on Hadamard manifolds, such as strong consistency and joint asymptotic normality. We provide a detailed description of how to compute quantiles using a gradient descent algorithm in hyperbolic space and, in particular, an explicit formula for the gradient of the quantile loss function, along with experiments using simulated and real single-cell RNA sequencing data.
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2025 1verdicts
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A data-based notion of quantiles on Hadamard spaces
A data-based geometric quantile loss on Hadamard spaces is defined and shown to improve on parameter-based quantiles in consistency, asymptotic normality, robustness, extreme behavior, and computability.