SPATIAL QUANTILE AUTOREGRESSION FOR SEASON WITHIN YEAR DAILY MAXIMUM TEMPERATURE DATA

Regression is the most widely used modeling tool in statistics. Quantile regression offers a strategy for enhancing the regression picture beyond cus-tomary mean regression. With time-series data, we move to quantile autore-gression and, finally, with spatially referenced time series, we move to space-time quantile regression. Here, we are concerned with the spatiotemporal evolution of daily maximum temperature, particularly with regard to extreme heat. Our motivating data set is 60 years of daily summer maximum temperature data over Aragón in Spain. Hence, we work with time on two scales— days within summer season across years—collected at geocoded station lo-cations. For a specified quantile, we fit a very flexible, mixed-effects autore-gressive model, introducing four spatial processes. We work with asymmetric Laplace errors to take advantage of the available conditional Gaussian rep-resentation for these distributions. Further, while the autoregressive model yields conditional quantiles, we demonstrate how to extract marginal quan-tiles with the asymmetric Laplace specification. Thus, we are able to interpo-late quantiles for any days within years across our study region.

Duke Scholars

Author Alan E. Gelfand Statistical Science