PartCensReg: Estimation and Diagnostics for Partially Linear Censored Regression Models Based on Heavy-Tailed Distributions

It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.

Version: 1.39
Imports: ssym, optimx, Matrix
Suggests: SMNCensReg, AER
Published: 2018-03-08
Author: Marcela Nunez Lemus, Christian E. Galarza, Larissa Avila Matos, Victor H Lachos
Maintainer: Marcela Nunez Lemus <marcela.nunez.lemus at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: PartCensReg results

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Reference manual: PartCensReg.pdf
Package source: PartCensReg_1.39.tar.gz
Windows binaries: r-prerelease: PartCensReg_1.39.zip, r-release: PartCensReg_1.39.zip, r-oldrel: PartCensReg_1.39.zip
macOS binaries: r-prerelease: PartCensReg_1.39.tgz, r-release: PartCensReg_1.39.tgz, r-oldrel: PartCensReg_1.39.tgz
Old sources: PartCensReg archive

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