Estimating the index of increase via balancing deterministic and random data

TL;DR

This paper introduces an empirical index of increase that assesses monotonicity in deterministic and noisy data, providing consistency proofs and guidance on observation frequency for desired accuracy.

Contribution

It presents a novel index of increase applicable to both deterministic and stochastic data, with proven consistency and convergence rate analysis.

Findings

The index is consistent in both deterministic and random settings.

Convergence rate depends on data's deterministic and random components.

Guidelines for observation frequency to achieve target estimation precision.

Abstract

We introduce and explore an empirical index of increase that works in both deterministic and random environments, thus allowing to assess monotonicity of functions that are prone to random measurement-errors. We prove consistency of the index and show how its rate of convergence is influenced by deterministic and random parts of the data. In particular, the obtained results suggest a frequency at which observations should be taken in order to reach any pre-specified level of estimation precision. We illustrate the index using data arising from purely deterministic and error-contaminated functions, which may or may not be monotonic.