lax-package {lax}R Documentation

lax: Loglikelihood Adjustment for Extreme Value Models

Description

Performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages eva, evd, evir, extRemes, fExtremes, ismev, mev, POT and texmex. Univariate extreme value models, including regression models, are supported. Adjusted standard errors and an adjusted loglikelihood are provided, using the chandwich package and the object-oriented features of the sandwich package.

Details

The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007). This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification.

The main function is alogLik, which works in an object-oriented way, operating on fitted model objects. This function performs the loglikelihood adjustments using adjust_loglik. See the following package-specific help pages for details and examples: eva, evd, evir, extRemes, fExtremes, ismev, mev, POT, texmex.

See vignette("lax-vignette", package = "lax") for an overview of the package.

Author(s)

Maintainer: Paul J. Northrop p.northrop@ucl.ac.uk [copyright holder]

Authors:

References

Bader, B. and Yan, J. (2020). eva: Extreme Value Analysis with Goodness-of-Fit Testing. R package version 0.2.6. https://CRAN.R-project.org/package=eva

Belzile, L., Wadsworth, J. L., Northrop, P. J., Grimshaw, S. D. and Huser, R. (2019). mev: Multivariate Extreme Value Distributions. R package version 1.12.2. https://github.com/lbelzile/mev/

Berger S., Graham N., Zeileis A. (2017). Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R. Technical Report 2017-12, Working Papers in Economics and Statistics, Research Platform Empirical and Experimental Economics, Universitat Innsbruck. https://EconPapers.RePEc.org/RePEc:inn:wpaper:2017-12.

Chandler, R. E. and Bate, S. (2007). Inference for clustered data using the independence loglikelihood. Biometrika, 94(1), 167-183. doi:10.1093/biomet/asm015

Gilleland, E. and Katz, R. W. (2016). extRemes 2.0: An Extreme Value Analysis Package in R. Journal of Statistical Software, 72(8), 1-39. doi:10.18637/jss.v072.i08

Northrop, P. J. and Chandler, R. E. (2018). chandwich: Chandler-Bate Sandwich Loglikelihood Adjustment. R package version 1.1. https://CRAN.R-project.org/package=chandwich.

Pfaff, B. and McNeil, A. (2018). evir: Extreme Values in R. R package version 1.7-4. https://CRAN.R-project.org/package=evir

Ribatet, M. and Dutang, C. (2019). POT: Generalized Pareto Distribution and Peaks Over Threshold. R package version 1.1-7. https://CRAN.R-project.org/package=POT

Southworth, H., Heffernan, J. E. and Metcalfe, P. D. (2017). texmex: Statistical modelling of extreme values. R package version 2.4. https://CRAN.R-project.org/package=texmex.

Stephenson, A. G. evd: Extreme Value Distributions. R News, 2(2):31-32, June 2002. https://CRAN.R-project.org/doc/Rnews/

Stephenson, A. G., Heffernan, J. E. and Gilleland, E. (2018). ismev: An Introduction to Statistical Modeling of Extreme Values. R package version 1.42. https://CRAN.R-project.org/package=ismev.

Wuertz, D., Setz, T. and Chalabi, Y. (2017). fExtremes: Rmetrics - Modelling Extreme Events in Finance. R package version 3042.82. https://CRAN.R-project.org/package=fExtremes

Zeileis A. (2004). Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software, 11(10), 1-17. doi:10.18637/jss.v011.i10.

Zeileis A. (2006). Object-Oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1-16. doi:10.18637/jss.v016.i09.

See Also

Useful links:

[Package lax version 1.2.3 Index]