Preprint Article Version 1 NOT YET PEER-REVIEWED

Defensive Signal Processing: The Case for the Use of Nonparametric and Robust Statistical Methods to Reduce Product Liability Exposure

Version 1 : Received: 13 September 2017 / Approved: 14 September 2017 / Online: 14 September 2017 (09:19:58 CEST)

How to cite: Lang, M. Defensive Signal Processing: The Case for the Use of Nonparametric and Robust Statistical Methods to Reduce Product Liability Exposure. Preprints 2017, 2017090054 (doi: 10.20944/preprints201709.0054.v1). Lang, M. Defensive Signal Processing: The Case for the Use of Nonparametric and Robust Statistical Methods to Reduce Product Liability Exposure. Preprints 2017, 2017090054 (doi: 10.20944/preprints201709.0054.v1).

Abstract

This paper makes the case that in an Internet of Things (IoT) world where data processing has become pervasive, the assessment of whether or not the underlying (statistical) modeling assumptions are justified and appropriate should no longer be limited to the perspective of mathematical statistics alone. The paper argues that large parts of sound academic research in engineering lack practical merit in that, akin to a concept car, they are not market-ready, most commonly due to feasibility and liability issues. Through an analysis of both statistical and legal aspects it will be shown that the stoic pursuit of ‘optimality’ more often than not yields to risky and suboptimal outcomes when applied to actual physical world problems. To address this, the concept of ‘Defensive Signal Processing’ is introduced and future research directions are briefly outlined.

Subject Areas

defensive signal processing; robust statistics; nonparametric statistics; model uncertainty; IoT; optimization; liability; tortious product liability; strict product liability

Readers' Comments and Ratings (0)

Leave a public comment
Send a private comment to the author(s)
Rate this article
Views 0
Downloads 0
Comments 0
Metrics 0
Leave a public comment

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.