expectreg (version 0.53)

expectreg-package: Expectile and Quantile Regression

Description

Expectile and quantile regression of models with nonlinear effects e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes as well as boosting; also supplies expectiles for common distributions.

Author

Fabian Otto-Sobotka
Carl von Ossietzky University Oldenburg
https://uol.de

Elmar Spiegel
Helmholtz Centre Munich
https://www.helmholtz-munich.de

Sabine Schnabel
Wageningen University and Research Centre
https://www.wur.nl

Linda Schulze Waltrup
Ludwig Maximilian University Munich
https://www.lmu.de

with contributions from

Paul Eilers
Erasmus Medical Center Rotterdam
https://www.erasmusmc.nl

Thomas Kneib
Georg August University Goettingen
https://www.uni-goettingen.de

Goeran Kauermann
Ludwig Maximilian University Munich
https://www.lmu.de

Maintainer: Fabian Otto-Sobotka <[e-mail]>

Details

• This package requires the packages BayesX, mboost, splines and quadprog.

References

Fenske N and Kneib T and Hothorn T (2009) Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression Technical Report 052, University of Munich

He X (1997) Quantile Curves without Crossing The American Statistician, 51(2):186-192

Koenker R (2005) Quantile Regression Cambridge University Press, New York

Schnabel S and Eilers P (2009) Optimal expectile smoothing Computational Statistics and Data Analysis, 53:4168-4177

Schnabel S and Eilers P (2011) Expectile sheets for joint estimation of expectile curves (under review at Statistical Modelling)

Sobotka F and Kneib T (2010) Geoadditive Expectile Regression Computational Statistics and Data Analysis, doi: 10.1016/j.csda.2010.11.015.

See Also

mboost, BayesX

Examples

data(dutchboys)

## Expectile Regression using the restricted approach
ex = expectreg.ls(dist ~ rb(speed),data=cars,smooth="f",lambda=5,estimate="restricted")
names(ex)

## The calculation of expectiles for given distributions
enorm(0.1)
enorm(0.5)

## Introducing the expectiles-meet-quantiles distribution
x = seq(-5,5,length=100)
plot(x,demq(x),type="l")

## giving an expectile analogon to the 'quantile' function
y = rnorm(1000)

expectile(y)

eenorm(y)