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Evaluation of Alternative Flexibility Restraint Procedures for Recursive Programming Models Used for Prediction
Author(s) -
Miller Thomas A.
Publication year - 1972
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.2307/1237735
Subject(s) - flexibility (engineering) , computer science , mathematical optimization , limit (mathematics) , linear programming , measure (data warehouse) , set (abstract data type) , recursive partitioning , algorithm , mathematics , statistics , machine learning , data mining , programming language , mathematical analysis
Recursive programming models for estimating production response may be generalized from the original “recursive” formulation to a formulation that simply attempts to limit the linear programming solution to a reasonable subset. This article outlines a framework for evaluating the effect of alternative flexibility restraint procedures on the accuracy of such recursive programming models. Statistical estimates are developed for the total expected error of predictions, given a specified set of population and model characteristics. The analysis suggests that recursive programming models achieve some measure of statistical accuracy.