Abstract
This paper is dedicated to the problem of the estimation of a vector of parameters, as losses resulting from their under- and overestimation are asymmetric and mutually correlated. The issue is considered from an additional conditional aspect, where particular coordinates of conditioning variables may be continuous, binary, discrete or categorized (ordered and unordered). The final result is an algorithm for calculating the value of an estimator, optimal in sense of expectation of losses using a multidimensional asymmetric quadratic function, for practically any distributions of describing and conditioning variables.
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Kulczycki, P., Charytanowicz, M. (2014). Conditional Multidimensional Parameter Identification with Asymmetric Correlated Losses of Estimation Errors. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_35
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DOI: https://doi.org/10.1007/978-3-319-12640-1_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12639-5
Online ISBN: 978-3-319-12640-1
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