English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a model-free nonparametric estimator of conditionalquantile of a time series regression model where the covariate vector isrepeated many times for different values of the response. This type of data isabound in climate studies. To tackle such problems, our proposed methodexploits the replicated nature of the data and improves on restrictive linearmodel structure of conventional quantile regression. Relevant asymptotic theoryfor the nonparametric estimators of the mean and variance function of the modelare derived under a very general framework. We provide a detailed simulationstudy which clearly demonstrates the gain in efficiency of the proposed methodover other benchmark models, especially when the true data generating processentails nonlinear mean function and heteroskedastic pattern with time dependentcovariates. The predictive accuracy of the non-parametric method is remarkablyhigh compared to other methods when attention is on the higher quantiles of thevariable of interest. Usefulness of the proposed method is then illustratedwith two climatological applications, one with a well-known tropical cyclonewind-speed data and the other with an air pollution data.

Nonparametric quantile regression for time series with replicated observations and its application to climate data