Greedy algorithms for prediction. / Sancetta, Alessio.

In: Bernoulli, Vol. 22, No. 2, 01.05.2016, p. 1227-1277.

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Abstract

In many prediction problems, it is not uncommon that the number of variables used to construct a forecast is of the same order of magnitude as the sample size, if not larger. We then face the problem of constructing a prediction in the presence of potentially large estimation error. Control of the estimation error is either achieved by selecting variables or combining all the variables in some special way. This paper considers greedy algorithms to solve this problem. It is shown that the resulting estimators are consistent under weak conditions. In particular, the derived rates of convergence are either minimax or improve on the ones given in the literature allowing for dependence and unbounded regressors. Some versions of the algorithms provide fast solution to problems such as Lasso.
Original languageEnglish
Pages (from-to)1227-1277
Number of pages51
JournalBernoulli
Volume22
Issue number2
Early online date9 Nov 2015
DOIs
Publication statusPublished - 1 May 2016
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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