EconPapers    
Economics at your fingertips  
 

Higher Order Asymptotics in Estimation

Masafumi Akahira ()
Additional contact information
Masafumi Akahira: University of Tsukuba, Institute of Mathematics

Chapter Chapter 4 in Theory of Statistical Estimation, 2026, pp 83-118 from Springer

Abstract: Abstract The development of the higher order asymptotic theory of statistical estimation is described and the structure of the theory is clarified. From the viewpoint of concentration probability of estimators around a true parameter, the asymptotic efficiency of estimators is discussed up to the higher order. In particular, the phenomenon “third order efficiency implies fourth order efficiency” is derived and applied to the bias-adjusted maximum likelihood estimator and the bias-adjusted generalized Bayes estimator. The results bring us the essence of higher order asymptotics.

Date: 2026
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:978-981-95-5339-6_4

Ordering information: This item can be ordered from
http://www.springer.com/9789819553396

DOI: 10.1007/978-981-95-5339-6_4

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 ().

 
Page updated 2026-05-29
Handle: RePEc:spr:sprchp:978-981-95-5339-6_4