Spatial quantile autoregression for season within year daily maximum temperature data

Castillo-Mateo, Jorge (Universidad de Zaragoza) ; Asín, Jesús (Universidad de Zaragoza) ; Cebrián, Ana C. (Universidad de Zaragoza) ; Gelfand, Alan E. ; Abaurrea, Jesús (Universidad de Zaragoza)

Resumen: 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 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 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.

Idioma: Inglés

DOI: 10.1214/22-AOAS1719

Año: 2023

Publicado en: Annals of Applied Statistics 17, 3 (2023), 2305-2325
ISSN: 1932-6157

Factor impacto JCR: 1.3 (2023)

Categ. JCR: STATISTICS & PROBABILITY rank: 65 / 168 = 0.387 (2023) - Q2 - T2

Factor impacto CITESCORE: 3.1 - Statistics and Probability (Q2) - Statistics, Probability and Uncertainty (Q2) - Modeling and Simulation (Q2)

Factor impacto SCIMAGO: 0.954 - Modeling and Simulation (Q1) - Statistics, Probability and Uncertainty (Q1) - Statistics and Probability (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)