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An Algorithm for Restricted Least Squares Regression

Richard L. Dykstra
Journal of the American Statistical Association
Vol. 78, No. 384 (Dec., 1983), pp. 837-842
DOI: 10.2307/2288193
Stable URL: http://www.jstor.org/stable/2288193
Page Count: 6
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An Algorithm for Restricted Least Squares Regression
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Abstract

A commonly occurring problem in statistics is that of minimizing a least squares expression subject to side constraints. Here a simple iterative algorithm is presented and shown to converge to the desired solution. Several examples are presented, including finding the closest concave (convex) function to a set of points and other general quadratic programming problems. The dual problem to the basic problem is also discussed and a solution for it is given in terms of the algorithm. Finally, extensions to expressions other than least squares are given.

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