A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring

R. Pourmousa; M. Rezapour; M. Mashinchi

Dependence Modeling (2015)

  • Volume: 3, Issue: 1, page 228-239, electronic only
  • ISSN: 2300-2298

Abstract

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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.

How to cite

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R. Pourmousa, M. Rezapour, and M. Mashinchi. "A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring." Dependence Modeling 3.1 (2015): 228-239, electronic only. <http://eudml.org/doc/275960>.

@article{R2015,
abstract = {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.},
author = {R. Pourmousa, M. Rezapour, M. Mashinchi},
journal = {Dependence Modeling},
keywords = {Multivariate selection distribution; Membership function; Fuzzy event; multivariate selection distribution; membership function; fuzzy event},
language = {eng},
number = {1},
pages = {228-239, electronic only},
title = {A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring},
url = {http://eudml.org/doc/275960},
volume = {3},
year = {2015},
}

TY - JOUR
AU - R. Pourmousa
AU - M. Rezapour
AU - M. Mashinchi
TI - A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring
JO - Dependence Modeling
PY - 2015
VL - 3
IS - 1
SP - 228
EP - 239, electronic only
AB - 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.
LA - eng
KW - Multivariate selection distribution; Membership function; Fuzzy event; multivariate selection distribution; membership function; fuzzy event
UR - http://eudml.org/doc/275960
ER -

References

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