Estimation and Testing of Learning Curves
提出一种新的学习曲线估计方法，基于最大似然原理处理一阶自相关，并用非嵌套检验选择模型形式，建议使用单位数据并注意自相关问题。
This article describes a new approach to learning curve estimation. Our approach is to formulate statistical procedures that conform to alternative learning curve theories. This leads to the development of nonlinear statistical models of the learning curves. For the three data sets analyzed, autocorrelation seems to be an important problem. Parameter estimates were derived using the maximum likelihood principle in the presence of first-order autocorrelation. Nonnested tests were used to select the appropriate formulation of the learning curve. Research conclusions are to use unit data when estimating a learning curve and to be prepared to treat autocorrelation if present.