SPATIAL QUANTILE AUTOREGRESSION FOR SEASON WITHIN YEAR DAILY MAXIMUM TEMPERATURE DATA

• 成果类型: Article
• 署名作者: Castillo-Mateo, Jorge; Asin, Jesus; Cebrian, Ana C.; Gelfand, Alan E.; Abaurrea, Jesus
• 署名单位: University of Zaragoza; Duke University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1719
• 发表日期: 2023
• 页码: 2305-2325
• 关键词: regression; inference; MODEL

摘要

Regression is the most widely used modeling tool in statistics. Quantile regression offers a strategy for enhancing the regression picture beyond customary mean regression. With time-series data, we move to quantile autoregression and, finally, with spatially referenced time series, we move to spacetime 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 Aragon in Spain. Hence, we work with time on two scales- days within summer season across years-collected at geocoded station locations. For a specified quantile, we fit a very flexible, mixed-effects autoregressive model, introducing four spatial processes. We work with asymmetric Laplace errors to take advantage of the available conditional Gaussian representation for these distributions. Further, while the autoregressive model yields conditional quantiles, we demonstrate how to extract marginal quantiles with the asymmetric Laplace specification. Thus, we are able to interpolate quantiles for any days within years across our study region.