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- W2018964583 abstract "The asymmetric Laplace likelihood naturally arises in the estimation of conditional quantiles of a response variable given covariates. The estimation of its parameters entails unconstrained maximization of a concave and non-differentiable function over the real space. In this note, we describe a maximization algorithm based on the gradient of the log-likelihood that generates a finite sequence of parameter values along which the likelihood increases. The algorithm can be applied to the estimation of mixed-effects quantile regression, Laplace regression with censored data, and other models based on Laplace likelihood. In a simulation study and in a number of real-data applications, the proposed algorithm has shown notable computational speed." @default.
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- W2018964583 date "2014-04-25" @default.
- W2018964583 modified "2023-09-30" @default.
- W2018964583 title "A gradient search maximization algorithm for the asymmetric Laplace likelihood" @default.
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- W2018964583 doi "https://doi.org/10.1080/00949655.2014.908879" @default.
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