Stochastic monotonicity of the MLE of exponential mean under different censoring schemes

In this paper, we present a general method which can be used in order to show that the maximum likelihood estimator (MLE) of an exponential mean θ is stochastically increasing with respect to θ under different censored sampling schemes. This propery is essential for the construction of exact confide...

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Bibliographic Details
Published in:Annals of the Institute of Statistical Mathematics 2009-09, Vol.61 (3), p.753-772
Main Authors: Balakrishnan, N., Iliopoulos, G.
Format: Article
Language:English
Subjects:
Economics
Finance
Hypothesis testing
Insurance
Management
Mathematics and Statistics
Random variables
Remakes & sequels
Statistics
Statistics for Business
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Summary:In this paper, we present a general method which can be used in order to show that the maximum likelihood estimator (MLE) of an exponential mean θ is stochastically increasing with respect to θ under different censored sampling schemes. This propery is essential for the construction of exact confidence intervals for θ via “pivoting the cdf” as well as for the tests of hypotheses about θ . The method is shown for Type-I censoring, hybrid censoring and generalized hybrid censoring schemes. We also establish the result for the exponential competing risks model with censoring.
ISSN:0020-3157
1572-9052
DOI:10.1007/s10463-007-0156-y