SEMIPARAMETRIC TWO-COMPONENT MIXTURE MODEL WITH A KNOWN COMPONENT: A CLASS OF ASYMPTOTICALLY NORMAL ESTIMATORS

Laurent Bordes 1, * Pierre Vandekerkhove 2
* Auteur correspondant
1 LMAP - Laboratoire de Mathématiques et de leurs Applications [Pau]
2 LAMA - Laboratoire d'Analyse et de Mathématiques Appliquées
Abstract : In this paper we consider a two-component mixture model one component of which has a known distribution while the other is only known to be symmetric. The mixture proportion is also an unknown parameter of the model. This mixture model class has proved to be useful to analyze gene expression data coming from microarray analysis. In this paper is proposed a general estimation method leading to a joint central limit result for all the estimators. Applications to basic testing problems related to this class of models are proposed, and the corresponding inference procedures are illustrated through some simulation studies.
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Mathematical Methods of Statistics, Allerton Press, Springer (link), 2010, 19 (1), pp.22-41. <10.3103/s1066530710010023>
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Laurent Bordes, Pierre Vandekerkhove. SEMIPARAMETRIC TWO-COMPONENT MIXTURE MODEL WITH A KNOWN COMPONENT: A CLASS OF ASYMPTOTICALLY NORMAL ESTIMATORS. Mathematical Methods of Statistics, Allerton Press, Springer (link), 2010, 19 (1), pp.22-41. <10.3103/s1066530710010023>. <hal-00174725>

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