Communications in Applied Mathematics and Computational Science

On inference of statistical regression models for extreme events based on incomplete observation data

Olga Kaiser and Illia Horenko

Full-text: Open access

Abstract

We present a computationally efficient, semiparametric, nonstationary framework for statistical regression analysis of extremes with systematically missing covariates based on the generalized extreme value (GEV) distribution. It is shown that the involved regression model becomes nonstationary if some of the relevant model covariates are systematically missing. The resulting nonstationarity and the ill-posedness of the inverse problem are resolved by deploying the recently introduced finite-element time-series analysis methodology with bounded variation of model parameters (FEM-BV). The proposed FEM-BV-GEV approach allows a well-posed problem formulation and goes beyond probabilistic a priori assumptions of methods for analysis of extremes based on, e.g., nonstationary Bayesian mixture models, smoothing kernel methods or neural networks. FEM-BV-GEV determines the significant resolved covariates, reveals directly their influence on the trend behavior in probabilities of extremes and reflects the implicit impact of missing covariates. We compare the FEM-BV-GEV approach to the state-of-the-art GEV-CDN methodology (based on artificial neural networks) on test cases and real data according to four criteria: (1) information content of the models, (2) robustness with respect to the systematically missing information, (3) computational complexity and (4) interpretability of the models.

Article information

Source
Commun. Appl. Math. Comput. Sci., Volume 9, Number 1 (2014), 143-174.

Dates
Received: 20 May 2013
Revised: 28 November 2013
Accepted: 31 March 2014
First available in Project Euclid: 20 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.camcos/1513732107

Digital Object Identifier
doi:10.2140/camcos.2014.9.143

Mathematical Reviews number (MathSciNet)
MR3212869

Zentralblatt MATH identifier
1328.62209

Subjects
Primary: 62G05: Estimation 62G32: Statistics of extreme values; tail inference 65R32: Inverse problems
Secondary: 65C50: Other computational problems in probability 62F03: Hypothesis testing

Keywords
generalized extreme-value distribution systematically missing information nonstationary time-series analysis nonparametric statistics finite-element method

Citation

Kaiser, Olga; Horenko, Illia. On inference of statistical regression models for extreme events based on incomplete observation data. Commun. Appl. Math. Comput. Sci. 9 (2014), no. 1, 143--174. doi:10.2140/camcos.2014.9.143. https://projecteuclid.org/euclid.camcos/1513732107


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