GLDreg: Fit GLD Regression Model and GLD Quantile Regression Model to Empirical Data

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile regression is a competitive flexible model compared to standard/quantile regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.

Version: 1.0.7
Depends: GLDEX (≥ 2.0.0.5), ddst, grDevices, graphics, stats
Suggests: MASS, quantreg
Published: 2017-02-28
Author: Steve Su, with contributions from: R core team for qqgld.default function.
Maintainer: Steve Su <allegro.su at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: GLDreg results

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Reference manual: GLDreg.pdf
Package source: GLDreg_1.0.7.tar.gz
Windows binaries: r-devel: GLDreg_1.0.7.zip, r-release: GLDreg_1.0.7.zip, r-oldrel: GLDreg_1.0.7.zip
OS X El Capitan binaries: r-release: GLDreg_1.0.7.tgz
OS X Mavericks binaries: r-oldrel: GLDreg_1.0.7.tgz
Old sources: GLDreg archive

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