Pith. sign in

REVIEW 1 cited by

Kendall's Tau for Functional Data Analysis

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1912.03725 v1 pith:YK7OGWUI submitted 2019-12-08 stat.ME

classification stat.ME
keywords datafunctionalapplicationsassociationhilbertkendallmethodspace
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We treat the problem of testing for association between a functional variable belonging to Hilbert space and a scalar variable. Particularly, we propose a distribution-free test statistic based on Kendall's Tau which is one of the most popular methods to determine the association between two random variables. The distribution of the test statistic under the null hypothesis of independence is established using the theory of U-statistics taking values in a Hilbert space. We also consider the case when the functional data is sparsely observed, a situation that arises in many applications. Simulations show that the proposed method outperforms the alternatives under several conditions demonstrating the robustness of our approach. We provide data applications that further consolidate the utility of our method.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation

    cs.CV 2025-05 conditional novelty 7.0 of 10

    A 3,068-prompt benchmark with per-instance Q&A scoring shows that current text-to-image models, including reasoning-enhanced ones, handle reasoning-driven prompts poorly, with mathematical reasoning near zero.

Pith tools