Shrinkage Estimation of the Proportion in Randomized Response.
S.E. Ahmed, V.K. Rohatgi (1996)
Metrika
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S.E. Ahmed, V.K. Rohatgi (1996)
Metrika
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Sara Van De Geer (2010)
ESAIM: Probability and Statistics
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We study the estimation of a linear integral functional of a distribution F, using i.i.d. observations which density is a mixture of a family of densities k(.,y) under F. We examine the asymptotic distribution of the estimator obtained by plugging the non parametric maximum likelihood estimator (NPMLE) of F in the functional. A problem here is that usually, the NPMLE does not dominate F. Our main aim here is to show that this can be overcome by considering a convex combination...
C. Radhakrishna Rao (1962)
Annales scientifiques de l'Université de Clermont. Mathématiques
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Costea, Nicolae, Cârlig, George (2003)
Analele Ştiinţifice ale Universităţii “Ovidius" Constanţa. Seria: Matematică
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A.K.Md.E. Saleh, T. Kubokawa (1994)
Metrika
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P. Peruničić, Z. Glišić (1987)
Matematički Vesnik
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M. Alama-Bućko, A. Zaigraev (2006)
Applicationes Mathematicae
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An asymptotic analysis, when the sample size n tends to infinity, of the optimal confidence region established in Czarnowska and Nagaev (2001) is considered. As a result, two confidence regions, both close to the optimal one when n is sufficiently large, are suggested with a mild assumption on the distribution of a location-scale family.
Ahmed, S.Ejaz, Li, Deli, Rosalsky, Andrew, Volodin, Andrei (2005)
Lobachevskii Journal of Mathematics
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Sonia Inacio, Manuela M. Oliveira, João Tiago Mexia (2015)
Discussiones Mathematicae Probability and Statistics
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We use asymptotic linearity to derive confidence intervals for large non-centrality parameters. These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics. We show how to use our approach by considering two sets of data as application examples.