Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false

Background: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. Results: We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Harvey J. Motulsky, Ronald E. Brown
• Keywords: BMCBI 2006, False Discovery Rate, Nonlinear Regression, Outliers