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Favorability functions based on kernel density estimation for logistic models: A case study

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  • Colubi, Ana
  • González-Rodri­guez, Gil
  • Domi­nguez-Cuesta, Mari­a José
  • Jiménez-Sánchez, Montserrat

Abstract

Susceptibility or hazard models are often established by means of logistic regression techniques in order to describe the effect of a group of explanatory variables on the probability of a dichotomous or binary response. Since the available variables do not always meet the assumptions of logit-linearity of the logistic regression, a modified approach is proposed. Firstly a favorability function associated with each explanatory variable based on the conditional probability measures is introduced. Next, a simple transformation based on the empirical probability function for non-continuous variables is suggested, while nonparametric kernel estimation is considered for continuous ones. The favorability-based transformations lead to new explanatory variables for the logistic regression model. The performance of the method is evaluated using simulated data. In addition, a real case-study is presented, in which a GIS-based landslides susceptibility model is carried out.

Suggested Citation

  • Colubi, Ana & González-Rodri­guez, Gil & Domi­nguez-Cuesta, Mari­a José & Jiménez-Sánchez, Montserrat, 2008. "Favorability functions based on kernel density estimation for logistic models: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4533-4543, May.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4533-4543
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    References listed on IDEAS

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