Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss

Mendo Tomás, Luis (2012). Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss. "Test", v. 21 (n. 4); pp. 656-675. ISSN 1133-0686. https://doi.org/10.1007/s11749-011-0267-x.

Descripción

Título: Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss
Autor/es:
  • Mendo Tomás, Luis
Tipo de Documento: Artículo
Título de Revista/Publicación: Test
Fecha: Diciembre 2012
Volumen: 21
Materias:
Palabras Clave Informales: Sequential estimation, Point estimator, Inverse binomial sampling, Asymmetric loss function.
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Ninguna

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Resumen

Sequential estimation of the success probability $p$ in inverse binomial sampling is considered in this paper. For any estimator $\hatvap$, its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitrary slope parameters $a$ and $b$ for $\hatvap < p$ and $\hatvap > p$ respectively. Interest in these functions is motivated by their significance and potential uses, which are briefly discussed. Estimators are given for which the risk has an asymptotic value as $p \rightarrow 0$, and which guarantee that, for any $p \in (0,1)$, the risk is lower than its asymptotic value. This allows selecting the required number of successes, $\nnum$, to meet a prescribed quality irrespective of the unknown $p$. In addition, the proposed estimators are shown to be approximately minimax when $a/b$ does not deviate too much from $1$, and asymptotically minimax as $\nnum \rightarrow \infty$ when $a=b$.

Más información

ID de Registro: 21857
Identificador DC: http://oa.upm.es/21857/
Identificador OAI: oai:oa.upm.es:21857
Identificador DOI: 10.1007/s11749-011-0267-x
URL Oficial: http://www.springer.com/statistics/journal/11749
Depositado por: Dr. Luis Mendo
Depositado el: 05 Dic 2013 09:35
Ultima Modificación: 21 Abr 2016 12:39
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