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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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0111 · Jun 202119922001200920172026
4 results for “center-outward q-dominance”
Center-Outward q-Dominance: A Sample-Computable Proxy for Strong Stochastic Dominance in Multi-Objective Optimisationcs.LG

New method ranks multivariate distributions in SMOOP using q-dominance.

problem Lack of reliable methods to rank multivariate distributions in SMOOP.
method Introduces center-outward q-dominance and develops empirical test procedures.
result Proves q-dominance implies FSD and establishes a sample size threshold.
stochastic multi-objective optimizationcenter-outward q-dominancestrong stochastic dominancehyperparameter tuningNSGA-II
2025-11-16abs ↗pdf ↗
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applicationscs.LG

This paper introduces depth functions for ranking data, improving statistical summaries.

problem Lack of comprehensive statistical summaries for ranking data.
method Metric-based depth functions on symmetric group to define rankings, depths, and procedures.
result Novel depth functions provide a more informative summary of ranking data.
ranking datadepth functionssymmetric groupstatistical summariesmetric-based
2022-01-20abs ↗pdf ↗
seMCD: Sequentially implemented Monte Carlo depth computation with statistical guaranteesstat.ME

seMCD computes depth functions with statistical guarantees using sequential Monte Carlo.

problem Computing depth functions is computationally challenging, especially in high dimensions.
method Sequential Monte Carlo methodology with theoretical and empirical guarantees.
result The seMCD method provides accurate depth approximations with fewer samples than traditional methods.
Monte Carlodepth functionssequential algorithmsstatistical guaranteesoutlier detection
2025-07-08abs ↗pdf ↗
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysisstat.ML

A new depth function improves multivariate data analysis by considering variability directions.

problem Developing a depth function that respects quantile properties and is affine-invariant.
method Integrating rank-weighted depth with affine-invariance and covariance matrices.
result The AI-IRW depth function provides accurate quantile estimates and is robust to data variability.
depth functionaffine-invariancemultivariate datastatistical depthanomaly detection
2021-06-21abs ↗pdf ↗