Estimation of Asymmetric Bell Shaped Probability Curve using Logistic Regression

로지스틱 회귀모형을 이용한 비대칭 종형 확률곡선의 추정

  • Published : 2001.03.01


Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.


  1. The Analysis of Binary Data Cox, D. R.
  2. An Introduction to Categorical Data Alan Agresti
  3. Data Mining Techniques Michael J. A. Berry;Gordon Linoff
  4. Applied logistic regression David W. Hosmer;Jr. Stanley Lemeshow
  5. Numerical Analysis Richard L. Burden;J. Douglas Faires