Abstract
Nonparametric predictive inference (NPI) is a powerful frequentist statistical framework which uses only few assumptions. Based on a post-data exchangeability assumption, precise probabilities for some events involving one or more future observations are defined, based on which lower and upper probabilities can be derived for all other events of interest. We present NPI for the r-th order statistic of m future real-valued observations and its use for comparison of two groups of data.
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Coolen, F.P.A., Maturi, T.A. (2010). Nonparametric Predictive Inference for Order Statistics of Future Observations. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_13
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DOI: https://doi.org/10.1007/978-3-642-14746-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14745-6
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