Skip to main content

How to Get the Most Accurate Measurement-Based Estimates

  • Chapter
  • First Online:
Uncertainty, Constraints, and Decision Making

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 484))

  • 267 Accesses

Abstract

In many practical situations, we want to estimate a quantity y that is difficult–or even impossible–to measure directly. In such cases, often, there are easier-to-measure quantities \(x_1,\ldots ,x_n\) that are related to y by a known dependence \(y=f\left( x_1,\ldots ,x_n\right) \). So, to estimate y, we can measure these quantities \(x_i\) and use the measurement results to estimate y. The two natural questions are: (1) within limited resources, what is the best accuracy with which we can estimate y, and (2) to reach a given accuracy, what amount of resources do we need? In this paper, we provide answers to these two questions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. S.G. Rabinovich, Measurement Errors and Uncertainty: Theory and Practice (Springer Verlag, New York, 2005)

    MATH  Google Scholar 

  2. D.J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures (Chapman and Hall/CRC, Boca Raton, Florida, 2011)

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and HRD-1834620 and HRD-2034030 (CAHSI Includes), and by the AT&T Fellowship in Information Technology.

It was also supported by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478, and by a grant from the Hungarian National Research, Development and Innovation Office (NRDI).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Robles, S., Ceberio, M., Kreinovich, V. (2023). How to Get the Most Accurate Measurement-Based Estimates. In: Ceberio, M., Kreinovich, V. (eds) Uncertainty, Constraints, and Decision Making. Studies in Systems, Decision and Control, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-36394-8_28

Download citation

Publish with us

Policies and ethics