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.
The content of this chapter was presented at the IMS 1967 Annual Meeting as an invited paper titled ‘Robust estimation and robust parameter’ (mimeographed) in a session on robust estimation. That has been developed and published as Bickel and Lehmann (1975) Descriptive statistics for nonparametric models. I. Introduction. Ann. Statist. 3, 1038–1044.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bickel, P.J., Lehmann, E.L.: Descriptive statistics for nonparametric models. I. Introduction. Ann. Stat. 3, 1038–1044 (1975)
Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35, 73–101 (1964)
Kallianpur, G., Rao, C.R.: On Fisher’s lower bound to asymptotic variance of a consistent estimate. Sankhya 15, 331–342 (1955)
Takeuchi, K.: Robust estimation and robust parameter. (mimeographed) Presented at Inst. Math. Statist. Ann. Meet. (1967)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Japan KK, part of Springer Nature
About this chapter
Cite this chapter
Takeuchi, K. (2020). Robust Estimation and Robust Parameter. In: Contributions on Theory of Mathematical Statistics. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55239-0_4
Download citation
DOI: https://doi.org/10.1007/978-4-431-55239-0_4
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-55238-3
Online ISBN: 978-4-431-55239-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)