NDLI: Theory of Domain Estimation of Statistical Parameters

Content Provider	Society for Industrial and Applied Mathematics (SIAM)
Author	Bunke, Olaf
Abstract	To estimate unknown parameters by using experimental results, we construct an estimation domain, a closed set in the parameter space. An estimation domain is characterized by the following kinds of losses: losses which occur when the estimation domain does not cover the true parametric point, and losses due to excessively large domain size (it is assumed that the size of the first loss can depend on the distance between the true parametric point and the estimation domain). To find an estimation domain with given properties, we construct a loss function and apply the method of decision functions. The theory of estimation domains represents an extension of the theory of interval and point estimates whose construction is not based on the theory of statistical hypothesis testing.
Starting Page	309
Ending Page	316
Page Count	8
File Format	PDF
ISSN	0040585X
DOI	10.1137/1108037
e-ISSN	10957219
Journal	Theory of Probability & Its Applications (TPRBAU)
Issue Number	3
Volume Number	8
Language	English
Publisher	Society for Industrial and Applied Mathematics
Publisher Date	2006-07-17
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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