Minimum Hellinger distance estimation in a semiparametric mixture model

In this report, we introduce the minimum Hellinger distance (MHD) estimation method and review its history. We examine the use of Hellinger distance to obtain a new efficient and robust estimator for a class of semiparametric mixture models where one component has known distribution while the other component and the mixing proportion are unknown. Such semiparametric mixture models have been used in biology and the sequential clustering algorithm. Our new estimate is based on the MHD, which has been shown to have good efficiency and robustness properties. We use simulation studies to illustrate the finite sample performance of the proposed estimate and compare it to some other existing approaches. Our empirical studies demonstrate that the proposed minimum Hellinger distance estimator (MHDE) works at least as well as some existing estimators for most of the examples considered and outperforms the existing estimators when the data are under contamination. A real data set application is also provided to illustrate the effectiveness of our proposed methodology.

Grantor dc:publisher

Kansas State University

Year dc:date.issued

2012

Author dc:creator

• Xiang, Sijia

• Semiparametric mixture models
• Minimum Hellinger distance
• Semiparametric EM algorithm

Statement dc:rights

• © the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/2097/13762

OAI record oai:identifier

https://krex.k-state.edu/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Akrex.k-state.edu%3A2097%2F13762