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An asymptotic approach to progressive censoring |
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| Authors: | Glenn Hofmann Erhard Cramer N. Balakrishnan Gerd Kunert |
| Affiliation: | aDepartamento de Estadistica, Facultad de Ciencias Físicas y Matemáticas, Barrio Universitario, Casilla 160-C, Concepción, Chile bDepartment of Mathematics, University of Oldenburg, 26111 Oldenburg, Germany cDepartment of Mathematics and Statistics, McMaster University, 1280 Main Street West, Hamilton, Ont., Canada L8S 4K1 dDepartment of Mathematics, TU Chemnitz, 09107 Chemnitz, Germany |
| Abstract: | Progressive Type-II censoring was introduced by Cohen (Technometrics 5(1963) 327) and has been the topic of much research. The question stands whether it is sensible to use this sampling plan by design, instead of regular Type-II right censoring. We introduce an asymptotic progressive censoring model, and find optimal censoring schemes for location-scale families. Our optimality criterion is the determinant of the 2×2 covariance matrix of the asymptotic best linear unbiased estimators. We present an explicit expression for this criterion, and conditions for its boundedness. By means of numerical optimization, we determine optimal censoring schemes for the extreme value, the Weibull and the normal distributions. In many situations, it is shown that these progressive schemes significantly improve upon regular Type-II right censoring. |
| Keywords: | Order statistics Progressive censoring Best linear estimation Generalized variance Optimal censoring scheme Extreme value distribution Weibull distribution Normal distribution |
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