Multiple Monte Carlo Testing with Applications in Spatial Point Processes

TL;DR

This paper introduces the rank envelope test, a method for multiple Monte Carlo testing that maintains correct error rates and provides graphical insights, applicable to spatial point processes and random sets.

Contribution

The paper presents a unified rank envelope test approach for multiple Monte Carlo tests, ensuring correct error control and offering graphical interpretation tools.

Findings

The rank envelope test controls the global type I error in various testing scenarios.

It provides p-values and graphical interpretations for test results.

Demonstrated effectiveness on spatial point process and random set examples.

Abstract

The rank envelope test (Myllym\"aki et al., Global envelope tests for spatial processes, arXiv:1307.0239 [stat.ME]) is proposed as a solution to multiple testing problem for Monte Carlo tests. Three different situations are recognized: 1) a few univariate Monte Carlo tests, 2) a Monte Carlo test with a function as the test statistic, 3) several Monte Carlo tests with functions as test statistics. The rank test has correct (global) type I error in each case and it is accompanied with a $p$-value and with a graphical interpretation which shows which subtest or which distances of the used test function(s) lead to the rejection at the prescribed significance level of the test. Examples of null hypothesis from point process and random set statistics are used to demonstrate the strength of the rank envelope test. The examples include goodness-of-fit test with several test functions, goodness-of-fit test for one group of point patterns, comparison of several groups of point patterns, test of dependence of components in a multi-type point pattern, and test of Boolean assumption for random closed sets.