A Criterion for Selecting Information-Preserving Data Reductions for Use in the Design of Multiple Parameter Estimators

Stephen P. Bruzzone; Mostafa Kaveh

doi:10.1109/TIT.1983.1056657

A Criterion for Selecting Information-Preserving Data Reductions for Use in the Design of Multiple Parameter Estimators

Stephen P. Bruzzone
, Mostafa Kaveh

Administration (CSENG)

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8 Scopus citations

Abstract

A scalar measure of the fractional amount of the total information for parameter estimation that is retained when a record of raw data is reduced to a given statistic is introduced. This measure, the relative information index (RII), is a function of Fisher's information matrices for the statistic and for the raw data, and it resembles an efficiency measure in that it acquires values between zero and one. A criterion, based on the RII, is formulated for statistic selection, and some applications are discussed.

Original language	English (US)
Pages (from-to)	466-470
Number of pages	5
Journal	IEEE Transactions on Information Theory
Volume	29
Issue number	3
DOIs	https://doi.org/10.1109/TIT.1983.1056657
State	Published - May 1983

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10.1109/TIT.1983.1056657

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abstract = "A scalar measure of the fractional amount of the total information for parameter estimation that is retained when a record of raw data is reduced to a given statistic is introduced. This measure, the relative information index (RII), is a function of Fisher's information matrices for the statistic and for the raw data, and it resembles an efficiency measure in that it acquires values between zero and one. A criterion, based on the RII, is formulated for statistic selection, and some applications are discussed.",

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AB - A scalar measure of the fractional amount of the total information for parameter estimation that is retained when a record of raw data is reduced to a given statistic is introduced. This measure, the relative information index (RII), is a function of Fisher's information matrices for the statistic and for the raw data, and it resembles an efficiency measure in that it acquires values between zero and one. A criterion, based on the RII, is formulated for statistic selection, and some applications are discussed.

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A Criterion for Selecting Information-Preserving Data Reductions for Use in the Design of Multiple Parameter Estimators

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