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.
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References
S.G. Rabinovich, Measurement Errors and Uncertainty: Theory and Practice (Springer Verlag, New York, 2005)
D.J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures (Chapman and Hall/CRC, Boca Raton, Florida, 2011)
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).
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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
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DOI: https://doi.org/10.1007/978-3-031-36394-8_28
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