The popular method to estimation the parameters of a linear regression model is the ordinary least square method which, despite the simplicity of calculating and providing the BLUE estimator of parameters, in some situations leads to misleading solutions. For example, we can mention the problems of multi-collinearity and outliers in the data set. The least trimmed squares method which is one of the most popular of robust regression methods decreases the influence of outliers as much as possible. The main goal of this paper is to provide a robust ridge estimation in order to model dental age data. Among the methods used to determine age, the most popular method throughout the world is the modern modified Demirjian method that is based on the calcification of the permanent tooth in panoramic radiography. It has been shown that using the robust ridge estimator is leading to reduce the mean squared error in comparison with the OLS method. Also, the proposed estimators were evaluated in simulated data sets.