A New Model for Learning Curves, DARM
提出一种位移自回归模型（DARM）用于学习曲线建模，相比传统指数模型和带自回归扰动的指数模型，该模型参数易估计、拟合更好，且能通过少量观测得到长期平均成本的合理估计，还可扩展以处理学习平台期。
This article introduces a new model for learning curves, a displaced autoregressive model (DARM). The model has theoretical and practical advantages over both the traditional exponential learning-curve model and the exponential leaming-curve model augmented by an autoregressive disturbance process. The DARM parameters can be related to the characteristics of the learning process and are easy to estimate. DARM appears to fit the data better than the alternative models, reasonable estimates of long-run average item costs can be obtained from a few observations, and the model can be modified to allow for learning plateaus.