Bayesian analysis for a constrained linear multiple regression problem for predicting the new crop of apples

In this article, a Bayesian model for a constrained linear regression problem is studied. The constraints arise naturally in the context of predicting the new crop of apples for the year ahead. We utilize the Gibbs sampler to obtain solutions to integration problems associated with Bayesian analysis...

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Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 1996-12, Vol.1 (4), p.467-489
Main Authors: Chen, M.H. (Polytechnic Institute, Worcester, MA.), Deely, J.J
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian networks
BAYESIAN THEORY
Boxes
Datasets
Density estimation
ESTIMATION
Linear regression
MALUS
Markov chains
MATHEMATICAL MODELS
METHODE STATISTIQUE
METODOS ESTADISTICOS
MODELE MATHEMATIQUE
Modeling
MODELOS MATEMATICOS
New crops
PLANT PRODUCTION
PRODUCCION VEGETAL
PRODUCTION VEGETALE
REGRESSION ANALYSIS
STATISTICAL METHODS
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Summary:In this article, a Bayesian model for a constrained linear regression problem is studied. The constraints arise naturally in the context of predicting the new crop of apples for the year ahead. We utilize the Gibbs sampler to obtain solutions to integration problems associated with Bayesian analysis. The Bayesian methodology with the Gibbs sampler is shown to be particularly suited to the constrained problem. Further, alternative methods such as ordinary and inequality-constrained least square estimations are investigated; and comparisons among Bayesian, ordinary, and inequality-constrained least square estimations are also made.
ISSN:1085-7117
1537-2693
DOI:10.2307/1400440