Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 892

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A Lego System for Conditional Inference

Hothorn, Torsten, Hornik, Kurt, van de Wiel, Mark A, Zeileis, Achim·2006·The American Statistician

Abstract

Conditioning on the observed data is an important and flexible design principle for statistical test procedures. Although generally applicable, permutation tests currently in use are limited to the treatment of special cases, such as contingency tables or K-sample problems. A new theoretical framework for permutation tests opens up the way to a unified and generalized view. This article argues that the transfer of such a theory to practical data analysis has important implications in many applications and requires tools that enable the data analyst to compute on the theoretical concepts as closely as possible. We reanalyze four datasets by adapting the general conceptual framework to these challenging inference problems and using the coin add-on package in the R system for statistical computing to show what one can gain from going beyond the “classical” test procedures.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1198/000313006x118430

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 892

Metadata

Authors
Hothorn, T., Hornik, K., van de Wiel, M. A., & Zeileis, A.
Venue
The American Statistician
Published
2006-08
Volume
60
Issue
3
Pages
257-263
ISSN
  • 0003-1305 (ISSN-L)
  • 1537-2731
OpenAlex
W2055992413
Cited by
892
References
2
FWCI
17.22
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Lego System for Conditional Inference. https://app.cochat.ai/papers/10.1198/000313006x118430
The paper
Hothorn, T., Hornik, K., van de Wiel, M. A., & Zeileis, A. (2006). A Lego System for Conditional Inference. The American Statistician. https://doi.org/10.1198/000313006x118430

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.