expectreg (version 0.26)

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.

Details

ll{ Package: expectreg Type: Package Version: 0.26 Date: 2011-09-08 License: GPL-2 LazyLoad: yes LazyData: yes }

• This package requires the packagesBayesX,mboost,splinesandquadprog.

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 = expectile.restricted(dist ~ base(speed),data=cars,smooth="s",lambda=5)
## The calculation of expectiles for given distributions
enorm(0.1)
## 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)