PartCensReg: Partially Censored Regression Models Based on Heavy-Tailed Distributions

It estimates the parameters of a partially censored regression model via maximum penalized likelihood through a iterative EM-type algorithm. The model must belong to the semi-parametric family, 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's-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: 1.38
Imports: ssym, optimx, Matrix
Suggests: SMNCensReg, AER
Published: 2018-01-05
Author: Marcela Nunez Lemus, Christian E. Galarza, Larissa Avila Matos, Victor H Lachos
Maintainer: Marcela Nunez Lemus <ra162510 at ime.unicamp.br>
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_1.38.tar.gz
Windows binaries: r-devel: PartCensReg_1.38.zip, r-release: PartCensReg_1.38.zip, r-oldrel: PartCensReg_1.38.zip
OS X El Capitan binaries: r-release: PartCensReg_1.38.tgz
OS X Mavericks binaries: r-oldrel: not available

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