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

Author

Listed:
  • Pourmousa R.
  • Rezapour M.
  • Mashinchi M.

    (Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran)

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.

Suggested Citation

  • Pourmousa R. & Rezapour M. & Mashinchi M., 2015. "A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring," Dependence Modeling, De Gruyter, vol. 3(1), pages 1-12, November.
    • Handle: RePEc:vrs:demode:v:3:y:2015:i:1:p:12:n:16
    DOI: 10.1515/demo-2015-0016
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