In the statistical literature, truncated distributions can be used for modeling real data. Due to error of measurement in truncated continuous data, choosing a crisp trimmed point caucuses a fault inference, so using fuzzy sets to define a threshold pointmay leads us more efficient results with respect to crisp thresholds. Arellano-Valle et al. [2] defined a selection distribution for analysis of truncated data with crisp threshold. In this paper, we define fuzzy multivariate selection distribution that is an extension of the selection distributions using fuzzy threshold. A practical data set with a fuzzy threshold point is considered to investigate the relationship between high blood pressure and BMI.
@article{bwmeta1.element.doi-10_1515_demo-2015-0016,
author = {R. Pourmousa and M. Rezapour and M. Mashinchi},
title = {A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring},
journal = {Dependence Modeling},
volume = {3},
year = {2015},
zbl = {1332.62060},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_1515_demo-2015-0016}
}
R. Pourmousa; M. Rezapour; M. Mashinchi. A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring. Dependence Modeling, Tome 3 (2015) . http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_1515_demo-2015-0016/
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