International Journal of Fuzzy Systems

, Volume 19, Issue 5, pp 1635–1644 | Cite as

Logistic Regression for Fuzzy Covariates: Modeling, Inference, and Applications

  • Fatemeh Salmani
  • S. Mahmoud Taheri
  • Jin Hee Yoon
  • Alireza Abadi
  • Hamid Alavi Majd
  • Abbas Abbaszadeh
Article

Abstract

Logistic regression is an important tool to evaluate the functional relationship between a binary response variable and a set of predictors. However, in clinical studies, often there is insufficient precision or indefiniteness of state. Therefore, we need to explore some soft methods for inference when the variables are reported as imprecise quantities. In this regard, we propose a fuzzy regression model with fuzzy covariates for imprecise binary-based response. We apply a least-squares method to estimate the model parameters and a bootstrap method for both computing confidence intervals and testing the hypotheses for the model parameters. The proposed model is then applied for verification to a numerical example based on a real clinical study of the effect of beloved person’s voice on reducing patient pain during the chest tube removal after an open heart surgery. Finally, the proposed model is evaluated by a well-known goodness-of-fit index.

Keywords

Fuzzy logistic regression Fuzzy covariate Least-squares method Bootstrap Pain 

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Fatemeh Salmani
    • 1
  • S. Mahmoud Taheri
    • 2
  • Jin Hee Yoon
    • 3
  • Alireza Abadi
    • 4
  • Hamid Alavi Majd
    • 1
  • Abbas Abbaszadeh
    • 5
  1. 1.Department of Biostatistics, Faculty of Paramedical SciencesShahid Beheshti University of Medical SciencesTehranIran
  2. 2.Faculty of Engineering Science, College of EngineeringUniversity of TehranTehranIran
  3. 3.School of Mathematics and StatisticsSejong UniversitySeoulSouth Korea
  4. 4.Department of Community Medicine, Faculty of Medicine, Social Determinants of Health Research CenterShahid Beheshti University of Medical SciencesTehranIran
  5. 5.Department of Nursing, Nursing and Midwifery SchoolShahid Beheshti University of Medical SciencesTehranIran

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