Spline Functions in Data Analysis.
PRINCETON UNIV N J DEPT OF STATISTICS
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This paper discusses the approximation of non-exact data by smooth functions. It is shown that optimal approximations for a large class of criteria are spline functions, and that a sub-class of these are resistant to the presence of gross errors in the data. A computational procedure for obtaining the optimal splines is described and illustrated on a set of demographic data. A listing of an APL program implementing the procedure is included.
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