A bayesian analysis for less smooth departures from polynomial regression models |
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Authors: | Daniel Barry |
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Institution: | Department of Mathematics and Statistics , University of Limerick , Limerick, Ireland |
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Abstract: | Observations yi are made at points ti according to the model i=θ(ti)+ei where the ei are independent normals with constant variance. It is conjec tured that the function θ lies in a set G of functions spanned by the basis functions φ1,φ2,…,φp. A prior distribution is developed whereby the proba bility assigned to a function θ is a decreasing function of a particular measure of the distance of θ from the set G. Bayes' theorem is used to construct an estimateθ. A marginal likelihood is derived which is used to estimate the parameter of the prior and also for testing the null hypothesis Ho θ ? G. The new methodology is tested in a Monte Carlo study and applied to a set of data representing the average weight to height ratio of a group of boys recorded at one month intervals. |
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Keywords: | Bayesian analysts: smoothing splines generalized maximum likelihood basis functions |
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