Abstract
A set of data may be ‘coarsened’ as a result of enumerators’ or compilers’ efforts to estimate (or falsify) observations. This type of coarsening typically results in excesses of ‘convenient’ numbers in the data sets, such as multiples of 5 or 10 in decimal number systems, apparent as patterns of periodic unit-width spikes in the frequency distributions.
We report on the development of novel Radial Basis Function neural-network techniques for detecting numerical data coarsened by rounding/estimation (or falsification) and quantifying that rounding/estimation. The objective is to provide an alternative to conventional statistical approaches based on missing-data techniques: data coarsened thus are not actually missing, solely shifted in size. The results show that the neural networks can successfully detect and classify the coarsening in data-sets and, hence, yield insights into the ways in which people count when performing enumeration or other numerical data-compilation exercises.
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Triastuti, E., Crockett, R., Picton, P., Crockett, A. (2004). Neural Network Analysis of Estimation of Data. In: Lotfi, A., Garibaldi, J.M. (eds) Applications and Science in Soft Computing. Advances in Soft Computing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45240-9_5
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DOI: https://doi.org/10.1007/978-3-540-45240-9_5
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