Time-adaptive quantile regression

Jan Kloppenborg Møller, Henrik Aalborg Nielsen, Henrik Madsen

    Research output: Contribution to journalJournal articleResearchpeer-review


    An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered.
    Original languageEnglish
    JournalComputational Statistics & Data Analysis
    Issue number3
    Pages (from-to)1292-1303
    Publication statusPublished - 2008


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