An Alternative Algorithm for Linear Regression Modeling for Efficient Decision: A New Strategy of Handling Insurance Data

Authors

  • Mohamad Arif Awang Nawi
  • Wan Muhamad Amir W Ahmad
  • Mohamad Shafiq Mohd Ibrahim
  • Mustafa Mamat
  • Rabiatul Adawiyah Abdul Rohim
  • Mohamad Afendee Mohamed
  • . .

DOI:

https://doi.org/10.14419/ijet.v7i3.28.27382

Keywords:

Multiple Linear Regression, Bootstrap method, Fuzzy Regression.

Abstract

The multiple linear regression model is an important tool for investigating relationships between several response variables and some predictor variables. The primary interest is in inference about the unknown regression coefficient matrix. In this paper, we propose to combine and compare multiple linear regression, bootstrapping and fuzzy regression methods to build alternative methods. We formalize this extension and prove its validity. A real data example and two simulated data examples, which offer some finite sample verification of our theoretical results are provided. The results, based on significant value and average width showed alternative methods produce better results than multiple linear regressions (MLR) model.

 

 

References

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How to Cite

Arif Awang Nawi, M., Muhamad Amir W Ahmad, W., Shafiq Mohd Ibrahim, M., Mamat, M., Adawiyah Abdul Rohim, R., Afendee Mohamed, M., & ., . (2018). An Alternative Algorithm for Linear Regression Modeling for Efficient Decision: A New Strategy of Handling Insurance Data. International Journal of Engineering & Technology, 7(3.28), 343–347. https://doi.org/10.14419/ijet.v7i3.28.27382

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Received 2019-02-12
Accepted 2019-02-12