The Annals of Statistics

Volume 29, Number 5

October 2001

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1999 Reitz Lecture

Greedy function approximation: A gradient boosting machine.Jerome H. Friedman; 1189 - 1232
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Mixture Models

Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densitiesSubhashis Ghosal and Aad W. van der Vaart; 1233 - 1263
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Convergence rates for density estimation with Bernstein polynomialsSubhashis Ghosal; 1264 - 1280
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Consistent estimation of mixture complexityLancelot F. James, David J. Marchette, and Carey E. Priebe; 1281 - 1296
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Nonparametric and Semiparametric Inference

Multiscale maximum likelihood analysis of a semiparametric model, with applicationsGuenther Walther; 1297 - 1319
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Smallest nonparametric tolerance regionsAlessandro Di Bucchianico, John H. Einmahl, and Nino A. Mushkudiani; 1320 - 1343
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A generalized additive regression model for survival timesThomas H. Scheike; 1344 - 1360
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Nonparametric analysis of covarianceHolger Dette and Natalie Neumeyer; 1361 - 1400
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Nonparametric estimation of the spectral measure of an extreme value distributionJohn H.J. Einmahl, Vladimir I. Piterbarg, and Laurens de Haan; 1401 - 1423
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Nearest neighbor classification with dependent training sequencesM. Holst and A. Irle; 1424 - 1442
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Bootstrap Estimation and Testing

Bootstrapping nonparametric density estimators with empirically chosen bandwidthsPeter Hall and Kee-Hoon Kang; 1443 - 1468
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Significance testing in nonparametric regression based on the bootstrapMiguel A. Delgado and Wenceslao González Manteiga; 1469 - 1507
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Sequential Estimation

On sequential estimation of parameters in semimartingale regression models with continuous time parameterL. Galtchouk and V. Konev; 1508 - 1536
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