CENSORED PAIRWISE LIKELIHOOD-BASED TESTS FOR MIXTURE PARAMETER OF SPATIAL MAX-MIXTURE MODELS

Abstract

Max-Mixture (MM) processes are defined as Z = max{aX, (1 − a)Y } with X an asymptotic depen- dent (AD) process, Y an asymptotic independent (AI) process and a ∈ [0, 1]. So that, the mixture parameter a controls the level of the AD part present in the MM process Z. Here we focus on two statistical tests for the mixing parameter a which are based on censored pairwise likelihood estimates.
We compare their performance through an extensive simulation study. Monte Carlo simulation are a fundamental tool for asymptotic variance calculations. We apply our tests to daily precipitations from the East of Australia. Limitations and possible developments of the approach are discussed.