Robust Switching Regressions Using the Laplace Distribution

This paper presents a robust method for dealing with switching regression problems. Regression models with switch-points are broadly employed in diverse areas. Many traditional methods for switching regressions can falter in the presence of outliers or heavy-tailed distributions because of the modeling assumptions of Gaussian errors. The outlier corruption of datasets is often unavoidable. When misapplied, the Gaussian assumption can lead to incorrect inference making. The Laplace distribution is known as a longer-tailed alternative to the normal distributions and connected with the robust least absolute deviation regression criterion. We propose a robust switching regression model of Laplace distributed errors. To advance robustness, we extend the Laplace switching model to a fuzzy class model and create a robust algorithm named FCL through the fuzzy classification maximum likelihood procedure. The robustness properties and the advance of resistance against high-leverage outliers are discussed. Simulations and sensitivity analyses illustrate the effectiveness and superiority of the proposed algorithm. The experimental results indicate that FCL is much more robust than the EM-based algorithm. Furthermore, the Laplace-based algorithm is more time-saving than the t-based procedure. Diverse real-world applications demonstrate the practicality of the proposed approach.