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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