Threshold estimation based on a P-value framework
Bodhisattva Sen, Moulinath Banerjee, George Michialidis

TL;DR
This paper introduces a p-value based method for estimating the threshold where a regression function departs from its baseline, applicable in various scientific fields, with proven consistency and demonstrated through simulations and real data.
Contribution
It proposes a novel, simple p-value framework for threshold estimation that is consistent and applicable to multiple responses and real-world data.
Findings
The method accurately estimates thresholds in simulated data.
It is computationally simple and effective in real data applications.
Extensions to multiple thresholds are feasible.
Abstract
We use p-values as a discrepancy criterion for identifying the threshold value at which a regression function takes off from its baseline value -- a problem that is motivated by applications in omics experiments, systems engineering, pharmacological dose-response studies and astronomy. In this paper, we study the problem in a controlled sampling setting, where multiple responses, discrete or continuous, can be obtained at a number of different covariate-levels. Our procedure involves testing the hypothesis that the regression function is at its baseline at each covariate value using the sampled responses at that value and then computing the p-value of the test. An estimate of the threshold is provided by fitting a stump, i.e., a piecewise constant function with a single jump discontinuity, to the observed p-values, since the corresponding p-values behave in markedly different ways on…
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Taxonomy
TopicsControl Systems and Identification · Statistical Methods and Inference · Image and Signal Denoising Methods
