PartCensReg-package {PartCensReg}

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

Description

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.

References

Ferreira, C. S., & Paula, G. A. (2017). Estimation and diagnostic for skew-normal partially linear models. Journal of Applied Statistics, 44(16), 3033-3053.

Ibacache-Pulgar, G., Paula, G. A., & Cysneiros, F. J. A. (2013). Semiparametric additive models under symmetric distributions. Test, 22(1), 103-121.

Ibacache-Pulgar, G., & Paula, G. A. (2011). Local influence for Student-t partially linear models. Computational Statistics & Data Analysis, 55(3), 1462-1478.

See Also

CensReg.SMN

Examples

dtawage = get(data(PSID1976,package = "AER"))
y  = dtawage$wage
cc = c(rep(0,428),rep(1,325))
tt = dtawage$exper
x  = cbind(dtawage$education,dtawage$age, dtawage$hhours, dtawage$hwage, dtawage$tax,
dtawage$youngkids, dtawage$oldkids)

#Normal case by default with only 10 iterations
PCR.default1 = Cens.SMN.PCR(x=x, y=y, c=cc, cens="left",tt =tt,iter.max = 10,Diagnostic = FALSE)

## Not run: 
#This may take few minutes
#Normal case by default with full (200) iterations
PCR.default2 = Cens.SMN.PCR(x=x, y=y, c=cc, cens="left",tt =tt)

#contaminated normal case
PCR.CN = Cens.SMN.PCR(x=x, y=y, c=cc, cens="left",tt =tt,type="NormalC",
nu = c(0.1,0.1),iter.max = 100)

## End(Not run)