In this paper, heteroscedastic nonlinear regression (HNLR) models under the flexible class of two–piece distributions based on the scale mixtures of normal (TP–SMN) family were examined. This novel class of nonlinear regression (NLR) models is a generalization of the well-known heteroscedastic symmetrical nonlinear regression models. The TP–SMN is a rich class of distributions that covers symmetric and asymmetric as well as heavy-tailed distributions. Using the suitable hierarchical representation of the family, the researchers first derived an EM–type algorithm for iteratively computing maximum likelihood (ML) estimates of the parameters. Then, in order to examine the performance of the proposed models and methods, some simulation studies were presented to show the robust aspect of this flexible class against outlying and also atypical data. As the last step, a natural real dataset was fitted under the proposed HNLR models.

本文研究了基于正态（TP - SMN）族的比例混合的两件式分布的柔性类别下的异方差非线性回归（HNLR）模型。这类新的非线性回归（NLR）模型是众所周知的异方差对称非线性回归模型的概括。在TP - SMN是富人阶层分布，涵盖对称和非对称以及重尾分布。使用家庭的合适的分层表示，研究人员首先导出的EM迭代地计算最大似然算法型（ML）参数的估算值。然后，为了检查所提出的模型和方法的性能，提出了一些仿真研究，以显示该灵活类针对异常数据和非典型数据的稳健方面。作为最后一步，在建议的HNLR模型下拟合了自然的真实数据集。