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Greedy Function Approximation: A Gradient Boosting
MachineAuthor(s): Jerome H. FriedmanSource: The Annals of
Statistics, Vol. 29, No. 5 (Oct., 2001), pp. 1189-1232Published by:
Institute of Mathematical StatisticsStable URL:
http://www.jstor.org/stable/2699986Accessed: 05/09/2008 10:04
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Article
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Issue Table of ContentsAnnals of Statistics, Vol. 29, No. 5,
Oct., 2001Front Matter1999 Reitz LectureGreedy Function
Approximation: A Gradient Boosting Machine [pp.1189-1232]
Mixture ModelsEntropies and Rates of Convergence for Maximum
Likelihood and Bayes Estimation for Mixtures of Normal Densities
[pp.1233-1263]Convergence Rates for Density Estimation with
Bernstein Polynomials [pp.1264-1280]Consistent Estimation of
Mixture Complexity [pp.1281-1296]
Nonparametric and Semiparametric InferenceMultiscale Maximum
Likelihood Analysis of a Semiparametric Model, with Applications
[pp.1297-1319]Smallest Nonparametric Tolerance Regions
[pp.1320-1343]A Generalized Additive Regression Model for Survival
Times [pp.1344-1360]Nonparametric Analysis of Covariance
[pp.1361-1400]Nonparametric Estimation of the Spectral Measure of
an Extreme Value Distribution [pp.1401-1423]Nearest Neighbor
Classification with Dependent Training Sequences [pp.1424-1442]
Bootstrap Estimation and TestingBootstrapping Nonparametric
Density Estimators with Empirically Chosen Bandwidths
[pp.1443-1468]Significance Testing in Nonparametric Regression
Based on the Bootstrap [pp.1469-1507]
Sequential EstimationOn Sequential Estimation of Parameters in
Semimartingale Regression Models with Continuous Time Parameter
[pp.1508-1536]
Back Matter
