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

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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.

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Authors and Affiliations

  1. Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Fatemeh Salmani & Hamid Alavi Majd

  2. Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

    S. Mahmoud Taheri

  3. School of Mathematics and Statistics, Sejong University, Seoul, South Korea

    Jin Hee Yoon

  4. Department of Community Medicine, Faculty of Medicine, Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Alireza Abadi

  5. Department of Nursing, Nursing and Midwifery School, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Abbas Abbaszadeh

Authors
  1. Fatemeh Salmani
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  2. S. Mahmoud Taheri
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  3. Jin Hee Yoon
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  4. Alireza Abadi
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  5. Hamid Alavi Majd
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  6. Abbas Abbaszadeh
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Correspondence to Alireza Abadi.

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Salmani, F., Taheri, S.M., Yoon, J.H. et al. Logistic Regression for Fuzzy Covariates: Modeling, Inference, and Applications. Int. J. Fuzzy Syst. 19, 1635–1644 (2017). https://doi.org/10.1007/s40815-016-0258-x

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  • DOI: https://doi.org/10.1007/s40815-016-0258-x

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