Abstract
In the following we propose a lower bound on the risks of all estimators and then describe the asymptotic properties of the maximum likelihood, Bayes, and minimum distance estimators in the regular (smooth) case. We show that these estimators are consistent, asymptotically normal, and, in certain senses, asymptotically efficient. The general results are then illustrated on simple models of (mainly periodic) Poisson processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media New York
About this chapter
Cite this chapter
Kutoyants, Y.A. (1998). First Properties of Estimators. In: Statistical Inference for Spatial Poisson Processes. Lecture Notes in Statistics, vol 134. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1706-0_3
Download citation
DOI: https://doi.org/10.1007/978-1-4612-1706-0_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98562-6
Online ISBN: 978-1-4612-1706-0
eBook Packages: Springer Book Archive