Correction of Eye‐estimation by Using Shrunken Predictors | Zendy

Bouza C. N. | Zendy; Allende S. M. | Zendy

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Correction of Eye‐estimation by Using Shrunken Predictors

Author(s) -

Bouza C. N.,

Allende S. M.

Publication year - 1989

Publication title -

biometrical journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.108

H-Index - 63

eISSN - 1521-4036

pISSN - 0323-3847

DOI - 10.1002/bimj.4710310709

Subject(s) - kernel (algebra) , statistics , estimation , simple (philosophy) , simple linear regression , computer science , linear regression , regression , regression analysis , mathematics , population , artificial intelligence , algorithm , econometrics , medicine , engineering , philosophy , environmental health , systems engineering , epistemology , combinatorics

The eye‐estimation method is widely used in practice. Several agronomic and biological measures are currently estimated by this method. If a simple linear regression is the kernel model a shrinkage technique can be used for correcting the bias associated with this method. Two predictors of the population total are proposed and the corresponding model‐based errors are deduced. A simulation study fixes the behaviour of the predictors.

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