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In statistics, sieve estimators are a class of non-parametric estimator which use progressively more complex models to estimate an unknown high-dimensional function as more data becomes available, with the aim of asymptotically reducing error towards zero as the amount of data increases. This method is generally attributed to Ulf Grenander.
- Stuart Geman, Chii-Ruey Hwang. "Nonparametric Maximum Likelihood Estimation by the Method of Sieves" (PDF). The Annals of Statistics, Vol. 10, No. 2 (Jun., 1982), pp. 401-414.
- "Sieve Estimation" (PDF). Archived from the original (PDF) on September 2, 2006.
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