Bottai, M;
Orsini, N;
Geraci, M;
(2015)
A gradient search maximization algorithm for the asymmetric Laplace likelihood.
Journal of Statistical Computation and Simulation
, 85
(10)
pp. 1919-1925.
10.1080/00949655.2014.908879.
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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.
| Type: | Article |
|---|---|
| Title: | A gradient search maximization algorithm for the asymmetric Laplace likelihood |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/00949655.2014.908879 |
| Publisher version: | http://dx.doi.org/10.1080/00949655.2014.908879 |
| Language: | English |
| Additional information: | © 2014 The Author(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The moral rights of the named author(s) have been asserted. Permission is granted subject to the terms of the License under which the work was published. Please check the License conditions for the work which you wish to reuse. Full and appropriate attribution must be given. This permission does not cover any third party copyrighted material which may appear in the work requested. |
| UCL classification: | UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1424872 |
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