Robust Estimation and Robust Parameter

Kei Takeuchi ()
Additional contact information
Kei Takeuchi: Professor Emeritus, The University of Tokyo

Chapter Chapter 4 in Contributions on Theory of Mathematical Statistics, 2020, pp 89-101 from Springer

Abstract: Abstract This chapter is addressed to the problem of defining the parameter in a semiparametric situation. Suppose, for example, that the observation X is assumed to be expressed as $$X=\theta +\varepsilon $$, where $$\theta $$ is the parameter to be estimated and $$\varepsilon $$ is the error whose distribution is not specified by a finite number of parameters. Although the distribution of $$\varepsilon $$ is not specified, it must satisfy some condition to guarantee that the observation be ‘unbiased’ in one sense or another. Usual assumption of ‘unbiasedness’ in the sense that the expectation of $$\varepsilon $$ being zero, is not necessarily appropriate, since it sometimes happens that $$\varepsilon $$ may not have the expectation. In this chapter the problem is discussed by considering the parameter as a functional of the distribution function of X.

Date: 2020
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:978-4-431-55239-0_4

Ordering information: This item can be ordered from
http://www.springer.com/9784431552390

DOI: 10.1007/978-4-431-55239-0_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 ().