Journal of Biometrics & Biostatistics

Tabatabai MA

Publications

• Research Article
  A New Robust Method for Nonlinear Regression
  Author(s): Tabatabai MA, Kengwoung-Keumo JJ, Eby WM, Bae S, Manne U, Fouad M and Singh KPTabatabai MA, Kengwoung-Keumo JJ, Eby WM, Bae S, Manne U, Fouad M and Singh KP
  
  Background: When outliers are present, the least squares method of nonlinear regression performs poorly. The main purpose of this paper is to provide a robust alternative technique to the Ordinary Least Squares nonlinear regression method. This new robust nonlinear regression method can provide accurate parameter estimates when outliers and/or influential observations are present. Method: Real and simulated data for drug concentration and tumor size-metastasis are used to assess the performance of this new estimator. Monte Carlo simulations are performed to evaluate the robustness of our new method in comparison with the Ordinary Least Squares method. Results: In simulated data with outliers, this new estimator of regression parameters seems to outperform the Ordinary Least Squares with respect to bias, mean squared errors, and mean estimated parameters. Two algorithms have been propo.. Read More»
  DOI: 10.4172/2155-6180.1000199

  Abstract PDF