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Bivariate censored regression relying on a new estimator of the joint distribution function
- Publication Year :
- 2011
- Publisher :
- HAL CCSD, 2011.
-
Abstract
- In this paper we study a class of M-estimators in a regression model under bivariate random censoring and provide a set of sufficient conditions that ensure asymptotic n 1 / 2 - convergence . The cornerstone of our approach is a new estimator of the joint distribution function of the censored lifetimes. A copula approach is used to modelize the dependence structure between the bivariate censoring times. The resulting estimators present the advantage of being easily computable. A simulation study enlighten the finite sample behavior of this technique.
- Subjects :
- Statistics and Probability
Bivariate censoring
Statistics::Theory
0211 other engineering and technologies
02 engineering and technology
Bivariate analysis
Statistics::Other Statistics
01 natural sciences
Copula (probability theory)
010104 statistics & probability
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Regression modeling
Econometrics
Statistics::Methodology
Joint distribution function
0101 mathematics
Kaplan–Meier estimator
$M-$estimation
Mathematics
Censored regression model
021103 operations research
Kaplan-Meier estimator
Applied Mathematics
Estimator
i.i.d. representations
Regression analysis
[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Statistics::Computation
Censoring (clinical trials)
Statistics, Probability and Uncertainty
Copula functions
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....64c8759d83507ceb5cb7d4810db4ae2f