A Fast and Efficient Algorithm for the Estimation of Parameters in Models with the Box-and-Cox Transformation
提出一种改进的牛顿算法，用于估计含Box-Cox变换的模型参数，相比其他梯度方法快2-4倍，适合需要高效估计非线性模型的经管研究者。
Abstract A modified Newton algorithm for the estimation of parameters in models containing the Box-Cox transformation is presented. It is shown that the usual maximum likelihood estimator for the k linear parameters and the m power transformation parameters may be specified as an m-parameter nonlinear least squares estimator. Several models containing Box-Cox transformations are estimated and the speed and efficiency of the modified algorithm compared with three other gradient estimation techniques. The modified Newton algorithm obtains the same parameter estimates, but two to four times faster.