A New Maximum Likelihood Algorithm for Piecewise Regression
提出一种处理含max或min算子的连续模型的分段回归方法，无需事先知道机制转换区域，利用解析导数最大化似然函数，简化估计并实现快速收敛。
Abstract This paper presents a piecewise regression method for continuous models containing max or min operators, or both. This method does not require knowledge of the zone in which a shift in regimes occurs. Moreover, it allows the application of analytical derivatives to maximize the likelihood function, which greatly simplifies the estimation of the model. The method proposed exhibits fast convergence and can be used for an arbitrary number of regimes and variables.