Confidence intervals for tree-structured varying coefficients

Nikolai Spuck, Matthias Schmid, Malte Monin and Moritz Berger

Computational Statistics & Data Analysis, 2025, vol. 207, issue C

Abstract: The tree-structured varying coefficient (TSVC) model is a flexible regression approach that allows the effects of covariates to vary with the values of the effect modifiers. Relevant effect modifiers are identified inherently using recursive partitioning techniques. To quantify uncertainty in TSVC models, a procedure to construct confidence intervals of the estimated partition-specific coefficients is proposed. This task constitutes a selective inference problem as the coefficients of a TSVC model result from data-driven model building. To account for this issue, a parametric bootstrap approach, which is tailored to the complex structure of TSVC, is introduced. Finite sample properties, particularly coverage proportions, of the proposed confidence intervals are evaluated in a simulation study. For illustration, applications to data from COVID-19 patients and from patients suffering from acute odontogenic infection are considered. The proposed approach may also be adapted for constructing confidence intervals for other tree-based methods.

Keywords: Varying coefficients; Tree-based modeling; Selective inference; Parametric bootstrap (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947325000180
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:207:y:2025:i:c:s0167947325000180

DOI: 10.1016/j.csda.2025.108142

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().