An Efficient Algorithm for REML in Heteroscedastic Regression∗
نویسنده
چکیده
This paper 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 which ensures that the REML-likelihood increases at each iteration. This paper shows how the complete computation, including the REML information matrix, may be carried out in O(n) operations.
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