Sieve estimator

Sieve estimator

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.

See also

* Nonparametric regression

External links

* cite web
url = http://www.dam.brown.edu/people/geman/Homepage/Mathematical%20statistics/Nonparametric%20MLE%20Sieves.pdf
title = Nonparametric Maximum Likelihood Estimation by the Method of Sieves
author = Stuart Geman, Chii-Ruey Hwang
publisher = The Annals of Statistics, Vol. 10, No. 2 (Jun., 1982), pp. 401-414
* cite web
url = http://www.ssc.wisc.edu/~dkristen/Econ715/Econ715_files/Econ%20715-ch9-sieves.pdf
title = Sieve Estimation


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