Score function of distribution and revival of the moment method

Zdeněk Fabián

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 4, 1118-1136

Abstract: The article deals with the scalar-valued score function SF defined for a regular unimodal and continuous probability distribution F with arbitrary interval support X∈R${\cal X} \in {\mathbb {R}}$, recently introduced by the author. The concept of the central characteristic that describes a relative influence of x∈X$x \in {\cal X}$ is described in a much more general way in order to improve its usefulness in parametric estimation by means of the general moment method. In particular, the whole approach is elucidated by describing it in a more suitable framework than before. Further, we show that the inference function to be used in the moment estimating equations is either the score function of distribution (bounded for heavy-tailed parametric distribution models) or its modification based on the Huber’s approach.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2013.857688 (text/html)
Access to full text is restricted to subscribers.

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: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:45:y:2016:i:4:p:1118-1136

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2013.857688

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().