imputeYn-package: Imputing the Last Largest Censored Observation(s) Under...

Description Details Author(s) References Examples

Description

Method brings less bias and more efficient estimates for AFT models.

Details

Package: imputeYn
Type: Package
Version: 1.3
Date: 2015-10-23
License: GPL
Depends: emplik, mvtnorm, quadprog, survival, boot

Author(s)

Hasinur Rahaman Khan and Ewart Shaw Maintainer: Hasinur Rahaman Khan <[email protected]>

References

Efron, B. (1967). The two sample problem with censored data. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 4, p. 831-853.

Jin et al. (2006). On least-squares regression with censored data. Biometrika, 93 (1), 147-161.

Khan and Shaw. (2013a). On Dealing with Censored Largest Observations under Weighted Least Squares. CRiSM working paper, Department of Statistics, University of Warwick, UK, No. 13-07. Also available in http://arxiv.org/abs/1312.2533.

Khan and Shaw (2013b). Variable Selection with The Modified Buckley-James Method and The Dantzig Selector for High-dimensional Survival Data. Proceedings 59th ISI World Statistics Congress, 25-30 August 2013, Hong Kong, p. 4239-4244.

Stute, W. (1993). Consistent estimation under random censorship when covariables are available. Journal of Multivariate Analysis, 45 , 89-103.

Examples

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#For uncorrelated dataset
data1<-data(n=100, p=4, r=0, b1=c(2,2,3,3), sig=1, Cper=0)
imp<-imputeYn(data1$x, data1$y, data1$delta, method = "condMean", beta=NULL)
imp

imputeYn documentation built on May 29, 2017, 2:18 p.m.