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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.