Comparative Analysis of Bootstrap Techniques for Confidence Interval Estimation in Spatial Covariance Parameters With Large Spatial Data

Zih‐Bing Chen, Hao‐Yun Huang and Cheng‐Xin Yang

Environmetrics, 2025, vol. 36, issue 3

Abstract: Inconsistent estimation issues in the Matérn covariance function pose significant challenges to constructing confidence intervals using traditional methods. This paper addresses these challenges by employing the bootstrap method and comparing two straightforward approaches: the percentile bootstrap (PB) and the reverse percentile interval (RPI). We assess their efficacy through coverage rates and interval scores, focusing on accuracy and breadth. Theoretically, we prove that PB outperforms RPI, a claim substantiated by simulation experiments showing its superior coverage accuracy and interval scores. Moreover, the simulation results show strongly interdependent phenomena between parameters. Accordingly, by exploring the micro‐ergodic parameter's impact, the study provides insights into these findings' underlying factors, particularly relevant for large spatial datasets. In the empirical study, our approach exhibits greater reliability and effectiveness in confidence interval estimation for large datasets with uniformly and non‐uniformly distributed locations, as compared to several other methods. Furthermore, we applied the method to sea surface temperature data, demonstrating its strong applicability for analysis. This study provides theoretical insight and practical guidance for constructing confidence intervals, particularly in mitigating inconsistent estimation issues, especially in the context of the Matérn covariance function.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/env.70015

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:wly:envmet:v:36:y:2025:i:3:n:e70015

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1180-4009

Access Statistics for this article

More articles in Environmetrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().