A model‐free approach for testing association

Saptarshi Chatterjee, Shrabanti Chowdhury and Sanjib Basu

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 3, 511-531

Abstract: The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis and inflammation, predictive of recurrence after lung resection in node‐negative non‐small cell lung cancer patients with tumour stage T2a or less. We propose an omnibus approach for testing the association that is free of assumptions on functional forms and distributions and can be used as a general method. This proposed maximal permutation test is based on the idea of thresholding, is readily implementable and is computationally efficient. We demonstrate that the proposed omnibus tests maintain their levels and have strong power for detecting linear, nonlinear and quantile‐based associations, even with outlier‐prone and heavy‐tailed error distributions and under nonparametric setting. We additionally illustrate the use of this approach in model‐free feature screening and further examine the level and power of these tests for binary outcome. We compare the performance of the proposed omnibus tests with comparator methods in our motivating application to identify the preoperative serum biomarkers associated with non‐small cell lung cancer recurrence in early stage patients.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12467

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:70:y:2021:i:3:p:511-531

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().