Modelling environmental data using bootstrap methods
R. Menezes, A. Monteiro, E. Silva
Environmental monitoring data often have a spatio-temporal structure. As a motivating example, we consider the primary pollutant NO2, collected in Portugal mainland, with different time resolutions.
Firstly, daily measurements along nine years are considered, aiming to understand seasonal patterns and to identify relevant spatial covariates (e.g. type of site and type of environment). Secondly, aiming to capture intra- and inter-day patterns imposed by social habits, we consider hourly data measured in those months when pollution levels are higher, being the importance of meteorological variables also investigated.
We present an easily implementable 2-stepwise approach to model spatio-temporal data, recommending to apply bootstrap methods to obtain accuracy measures of our parameters estimates. A parametric bootstrap or a block bootstrap procedure can be adopted to correctly assess uncertainty of these estimates, as well as to produce reliable confidence regions for NO2 levels.
Palabras clave / Keywords: environmental data, spatio-temporal data, bootstrap
Programado
Sesión bilateral SEIO-SPE: Statistics in Environmental Sciences and Health (Organizadores: Raquel Menezes y Carmen Cadarso)
30 de mayo de 2018 15:30
Sala Cristal
Otros trabajos en la misma sesión
E. Duarte, B. de Sousa, C. Cadarso-Suárez, V. Rodrigues, T. Kneib
J. Roca-Pardiñas, C. Ordóñez Galán
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