TUNE: Algorithm-Agnostic Inference after Changepoint Detection

TL;DR

TUNE is a versatile, algorithm-agnostic method for post-changepoint detection inference that controls error rates uniformly without relying on model-specific assumptions, enhancing reliability and robustness.

Contribution

It introduces TUNE, a novel universal thresholding approach that enables model-agnostic, error-controlled inference after changepoint detection, overcoming limitations of existing methods.

Findings

TUNE effectively controls family-wise error rate across various algorithms.

It demonstrates robustness and competitive power in theoretical and numerical tests.

TUNE offers a reliable, model-agnostic alternative for post-detection inference.

Abstract

In multiple changepoint analysis, assessing the uncertainty of detected changepoints is crucial for enhancing detection reliability -- a topic that has garnered significant attention. Despite advancements through selective p-values, current methodologies often rely on stringent assumptions tied to specific changepoint models and detection algorithms, potentially compromising the accuracy of post-detection statistical inference. We introduce TUNE (Thresholding Universally and Nullifying change Effect), a novel algorithm-agnostic approach that uniformly controls error probabilities across detected changepoints. TUNE sets a universal threshold for multiple test statistics, applicable across a wide range of algorithms, and directly controls the family-wise error rate without the need for selective p-values. Through extensive theoretical and numerical analyses, TUNE demonstrates versatility, robustness, and competitive power, offering a viable and reliable alternative for model-agnostic post-detection inference.