Robust Moment Based Estimation and Inference: The Generalized Cressie-Read Estimator

Ron C. Mittelhammer () and George G. Judge ()
Additional contact information
Ron C. Mittelhammer: Washington State University, School of Economic Sciences
George G. Judge: University of California-Berkeley

A chapter in Statistical Inference, Econometric Analysis and Matrix Algebra, 2009, pp 163-177 from Springer

Abstract: Abstract In this paper a range of information theoretic distance measures, based on Cressie-Read divergence, are combined with mean-zero estimating equations to provide an efficient basis for semi parametric estimation and testing. Asymptotic properties of the resulting semi parametric estimators are demonstrated and issues of implementation are considered.

Keywords: Empirical Likelihood; Reference Distribution; Empirical Likelihood Ratio; Extremum Metrics; Semi Parametric Estimation (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-7908-2121-5_11

Ordering information: This item can be ordered from
http://www.springer.com/9783790821215

DOI: 10.1007/978-3-7908-2121-5_11

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().