Abstract
Interval-valued observations arise in several real-life situations, and it is convenient to develop statistical methods to deal with them. In the literature on Statistical Inference with single-valued observations one can find different studies on drawing conclusions about the population mean on the basis of the information supplied by the available observations. In this paper we present a bootstrap method of testing a ‘two-sided’ hypothesis about the (interval-valued) mean value of an interval-valued random set based on an extension of the t statistic for single-valued data. The method is illustrated by means of a real-life example.
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Montenegro, M., Casals, M.R., Colubi, A., Gil, M.Á. (2008). Testing ‘Two-Sided’ Hypothesis about the Mean of an Interval-Valued Random Set. In: Dubois, D., Lubiano, M.A., Prade, H., Gil, M.Á., Grzegorzewski, P., Hryniewicz, O. (eds) Soft Methods for Handling Variability and Imprecision. Advances in Soft Computing, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85027-4_17
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DOI: https://doi.org/10.1007/978-3-540-85027-4_17
Publisher Name: Springer, Berlin, Heidelberg
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