Description: An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Packaged: 2014-02-09 20:34:01 UTC; Marek Hlavac

Packaged: 2014-03-21 01:33:07 UTC; Marek Hlavac

Packaged: 2014-05-23 06:41:31 UTC; Marek Hlavac

Packaged: 2014-11-18 15:30:01 UTC; Marek Hlavac

Imports: Formula, graphics, methods, stats, utils
Packaged: 2015-08-10 02:37:22 UTC; Marek Hlavac

Packaged: 2015-02-28 05:49:15 UTC; Marek Hlavac

person("Gilles", "Stupfler", role = "aut", email="gilles.stupfler.fr"))
Description: A set of procedures for estimating risks related to extreme events via risk measures such as expectile, value-at-risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2020) <arxiv:2004.04078>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>.

person("Gilles", "Stupfler", role = "aut", email="gilles.stupfler@ensai.fr"),
person("Bocconi Institute for Data Science and Analytics", role = "fnd"),
person("French National Research", role = "fnd", comment = "grant ANR-19-CE40-0013-01/ExtremReg"))
Bocconi Institute for Data Science and Analytics [fnd],
French National Research [fnd] (grant ANR-19-CE40-0013-01/ExtremReg)
Imports: evd, copula, mvtnorm, plot3D, tmvtnorm, pracma
Description: A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations.  The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2020) <arxiv:2004.04078>, Padoan and Stupfler (2020) <arxiv:2007.08944>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>.