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December 2019 Fire seasonality identification with multimodality tests
Jose Ameijeiras-Alonso, Akli Benali, Rosa M. Crujeiras, Alberto Rodríguez-Casal, José M. C. Pereira
Ann. Appl. Stat. 13(4): 2120-2139 (December 2019). DOI: 10.1214/19-AOAS1273


Understanding the role of vegetation fires in the Earth system is an important environmental problem. Although fire occurrence is influenced by natural factors, human activity related to land use and management has altered the temporal patterns of fire in several regions of the world. Hence, for a better insight into fires regimes it is of special interest to analyze where human activity has altered fire seasonality. For doing so, multimodality tests are a useful tool for determining the number of annual fire peaks. The periodicity of fires and their complex distributional features motivate the use of nonparametric circular statistics. The unsatisfactory performance of previous circular nonparametric proposals for testing multimodality justifies the introduction of a new approach, considering an adapted version of the excess mass statistic, jointly with a bootstrap calibration algorithm. A systematic application of the test on the Russia–Kazakhstan area is presented in order to determine how many fire peaks can be identified in this region. A False Discovery Rate correction, accounting for the spatial dependence of the data, is also required.


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Jose Ameijeiras-Alonso. Akli Benali. Rosa M. Crujeiras. Alberto Rodríguez-Casal. José M. C. Pereira. "Fire seasonality identification with multimodality tests." Ann. Appl. Stat. 13 (4) 2120 - 2139, December 2019.


Received: 1 October 2018; Revised: 1 May 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160933
MathSciNet: MR4037424
Digital Object Identifier: 10.1214/19-AOAS1273

Rights: Copyright © 2019 Institute of Mathematical Statistics


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Vol.13 • No. 4 • December 2019
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