Sieve estimator

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In statistics, sieve estimators are a class of nonparametric 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 U. Grenander.

[edit] See also

• Nonparametric regression

[edit] External links

• Stuart Geman, Chii-Ruey Hwang. Nonparametric Maximum Likelihood Estimation by the Method of Sieves. The Annals of Statistics, Vol. 10, No. 2 (Jun., 1982), pp. 401-414.
• Sieve Estimation.