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

TL;DR

This paper introduces a nonparametric, model-free estimator for conditional quantiles in time series data with repeated observations, particularly useful for climate studies, offering improved efficiency and accuracy over traditional methods.

Contribution

The paper develops a novel nonparametric quantile regression method that exploits data replication, addressing limitations of linear models in nonlinear and heteroskedastic climate data analysis.

Findings

Demonstrates improved efficiency over benchmark models in simulations.

Achieves high predictive accuracy for higher quantiles.

Successfully applied to climate data, including cyclone wind-speed and air pollution datasets.

Abstract

This paper proposes a model-free nonparametric estimator of conditional quantile of a time series regression model where the covariate vector is repeated many times for different values of the response. This type of data is abound in climate studies. To tackle such problems, our proposed method exploits the replicated nature of the data and improves on restrictive linear model structure of conventional quantile regression. Relevant asymptotic theory for the nonparametric estimators of the mean and variance function of the model are derived under a very general framework. We provide a detailed simulation study which clearly demonstrates the gain in efficiency of the proposed method over other benchmark models, especially when the true data generating process entails nonlinear mean function and heteroskedastic pattern with time dependent covariates. The predictive accuracy of the non-parametric method is remarkably high compared to other methods when attention is on the higher quantiles of the variable of interest. Usefulness of the proposed method is then illustrated with two climatological applications, one with a well-known tropical cyclone wind-speed data and the other with an air pollution data.