Editorial for the special issue on quantile regression and semiparametric methods
Computational Statistics & Data Analysis
Asymptotically efficient estimation of the conditional expected shortfall
Computational Statistics & Data Analysis
Modelling and forecasting wind speed intensity for weather risk management
Computational Statistics & Data Analysis
Learning uncertainty models from weather forecast performance databases using quantile regression
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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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.