Shrinkage Estimation of Proportion via Logit Penalty

Jung, Yoonsuh

Shrinkage Estimation of Proportion via Logit Penalty

dc.contributor.author	Jung, Yoonsuh	en_NZ
dc.date.accessioned	2016-05-05T23:28:50Z
dc.date.available	2016-04-05	en_NZ
dc.date.available	2016-05-05T23:28:50Z
dc.date.issued	2016-04-05	en_NZ
dc.description.abstract	By releasing the unbiasedness condition, we often obtain more accurate estimators due to the bias-variance tradeoff. In this paper, we propose a class of shrinkage proportion estimators which show improved performance over the sample proportion. We provide the “optimal” amount of shrinkage. The advantage of the proposed estimators is given theoretically as well as explored empirically by simulation studies and real data analyses.	en_NZ
dc.format.mimetype	application/pdf
dc.identifier.citation	Jung, Y. (2016). Shrinkage Estimation of Proportion via Logit Penalty. Communications in Statistics - Theory and Methods. http://doi.org/10.1080/03610926.2015.1048881	en
dc.identifier.doi	10.1080/03610926.2015.1048881	en_NZ
dc.identifier.issn	1532-415X	en_NZ
dc.identifier.uri	https://hdl.handle.net/10289/10182
dc.language.iso	en
dc.publisher	Taylor & Francis	en_NZ
dc.relation.isPartOf	Communications in Statistics - Theory and Methods	en_NZ
dc.subject	Biased estimator	en_NZ
dc.subject	Penalization
dc.subject	Sample proportion
dc.subject	Shrinkage proportion estimator
dc.title	Shrinkage Estimation of Proportion via Logit Penalty	en_NZ
dc.type	Journal Article
pubs.elements-id	138566
pubs.organisational-group	/Waikato
pubs.organisational-group	/Waikato/FCMS
pubs.publisher-url	http://www.tandfonline.com/doi/full/10.1080/03610926.2015.1048881	en_NZ