Generalized weighted likelihood density estimators with application to finite mixture of exponential family distributions

Tingting Zhan, Inna Chevoneva and Boris Iglewicz

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, pages 457-465

Abstract: The family of weighted likelihood estimators largely overlaps with minimum divergence estimators. They are robust to data contaminations compared to MLE. We define the class of generalized weighted likelihood estimators (GWLE), provide its influence function and discuss the efficiency requirements. We introduce a new truncated cubic-inverse weight, which is both first and second order efficient and more robust than previously reported weights. We also discuss new ways of selecting the smoothing bandwidth and weighted starting values for the iterative algorithm. The advantage of the truncated cubic-inverse weight is illustrated in a simulation study of three-component normal mixtures model with large overlaps and heavy contaminations. A real data example is also provided.

Keywords: Finite; normal; mixture; Generalized; weighted; likelihood; estimator; Influence; function; Smoothing; bandwidth; Truncated; cubic-inverse; weight; Weighted; starting; value (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6V8V ... 784fa825c760db3cd299
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:457-465

Access Statistics for this article

Computational Statistics & Data Analysis is edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Series data maintained by Wendy Shamier ().