Censored pairwise likelihood-based tests for mixing coefficient of spatial max-mixture models

TL;DR

This paper introduces censored pairwise likelihood-based tests to estimate the mixing coefficient in max-mixture spatial models, assessing their performance through simulations and real data application.

Contribution

It develops and compares new statistical tests for the mixing coefficient in max-mixture models, enhancing understanding of spatial dependence structures.

Findings

Tests perform well in simulations

Application to Australian precipitation data demonstrates practical utility

Discussion of limitations and future directions

Abstract

Max-mixture processes are defined as Z = max(aX, (1 -- a)Y) with X an asymptotic dependent (AD) process, Y an asymptotic independent (AI) process and a $\in$ [0, 1]. So that, the mixing coefficient a may reveal the strength of the AD part present in the max-mixture process. In this paper we focus on two tests based on censored pairwise likelihood estimates. We compare their performance through an extensive simulation study. Monte Carlo simulation plays a fundamental tool for asymptotic variance calculations. We apply our tests to daily precipitations from the East of Australia. Drawbacks and possible developments are discussed.