Multi-level Bayes and MAP monotonicity testing

TL;DR

This paper introduces Bayesian and MAP-based methods for monotonicity testing in functions, utilizing Haar transforms and multi-level adaptive tests to improve detection without prior segment size information.

Contribution

It develops new Bayesian and MAP approaches for monotonicity testing, linking it to sparse vector detection and constructing optimal multi-level tests.

Findings

Proposes Haar transform-based reduction to positivity testing.

Constructs adaptive multi-level tests without prior segment size knowledge.

Proves optimality of the proposed tests.

Abstract

In this paper, we develop Bayes and maximum a posteriori probability (MAP) approaches to monotonicity testing. In order to simplify this problem, we consider a simple white Gaussian noise model and with the help of the Haar transform we reduce it to the equivalent problem of testing positivity of the Haar coefficients. This approach permits, in particular, to understand links between monotonicity testing and sparse vectors detection, to construct new tests, and to prove their optimality without supplementary assumptions. The main idea in our construction of multi-level tests is based on some invariance properties of specific probability distributions. Along with Bayes and MAP tests, we construct also adaptive multi-level tests that are free from the prior information about the sizes of non-monotonicity segments of the function.