@article{(Open Science Index):https://publications.waset.org/pdf/10044,
	  title     = {A Renovated Cook's Distance Based On The Buckley-James Estimate In Censored Regression},
	  author    = {Nazrina Aziz and  Dong Q. Wang},
	  country	= {},
	  institution	= {},
	  abstract     = {There have been various methods created based on the regression ideas to resolve the problem of data set containing censored observations, i.e. the Buckley-James method, Miller-s method, Cox method, and Koul-Susarla-Van Ryzin estimators. Even though comparison studies show the Buckley-James method performs better than some other methods, it is still rarely used by researchers mainly because of the limited diagnostics analysis developed for the Buckley-James method thus far. Therefore, a diagnostic tool for the Buckley-James method is proposed in this paper. It is called the renovated Cook-s Distance, (RD* i ) and has been developed based on the Cook-s idea. The renovated Cook-s Distance (RD* i ) has advantages (depending on the analyst demand) over (i) the change in the fitted value for a single case, DFIT* i as it measures the influence of case i on all n fitted values Yˆ∗ (not just the fitted value for case i as DFIT* i) (ii) the change in the estimate of the coefficient when the ith case is deleted, DBETA* i since DBETA* i corresponds to the number of variables p so it is usually easier to look at a diagnostic measure such as RD* i since information from p variables can be considered simultaneously. Finally, an example using Stanford Heart Transplant data is provided to illustrate the proposed diagnostic tool.
},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {3},
	  number    = {5},
	  year      = {2009},
	  pages     = {328 - 333},
	  ee        = {https://publications.waset.org/pdf/10044},
	  url   	= {https://publications.waset.org/vol/29},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 29, 2009},
	}