An Efficient Algorithm for REML in Heteroscedastic Regression
针对异方差线性模型，提出一种REML评分算法，包含Levenberg-Marquardt修正以确保每次迭代似然值增加，且全部计算可在O(n)操作内完成，适用于需要高效估计的统计应用。
This article considers REML (residual or restricted maximum likelihood) estimation for heteroscedastic linear models. An explicit algorithm is given for REML scoring which yields the REML estimates together with their standard errors and likelihood values. The algorithm includes a Levenberg–Marquardt restricted step modification that ensures that the REML likelihood increases at each iteration. This article shows how the complete computation, including the REML information matrix, may be carried out in O(n) operations.