A simulation study to compare some estimation methods for the multiple change points segmented regression model

Abstract

Segmented regression is one of the statistical methods that are used to represent and model phenomena that go through several transitional stages. The basic concept of segmented regression depends on the presence of a certain point in the explanatory variable called (change point), and this point is shared and continuous between the ends of each two Consecutive transitional stages.In this research, the segmented regression model with multiple change points will be dealt with. the maximum likelihood estimator (MLE) and the two methods of robust repetitive weights (IRWm), (IRWs) will be used to estimate the model parameters and change points and then compare between these methods to choose the best method between them.A simulation process will be created with several scenarios, with different sample sizes and contamination rates (Outliers values) (15%, 10%, 5%, 0%) to Procedure the comparison process and find the best method for estimation.The simulation results, after completing the comparison process using the comparison standard, mean squared error (MSE), showed the efficiency of the Iterative Weights method (IRWm) when there were contamination rates in the data, and the efficiency of the maximum likelihood method (MLE) when the data did not contain any contamination.