Greedy model averaging

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Dai, Dong ^a   Zhang, Tong ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AVERAGING METHOD; EXPONENTIALS; LEAST SQUARES REGRESSION; MODEL AVERAGING; MODEL SELECTION; MULTIPLE-MODELING; PREDICTION ACCURACY; SAMPLE SIZES; SINGLE MODELS; WEIGHTED MODELS;

EID: 85162557989     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Jean-Yves Audibert. Progressive mixture rules are deviation suboptimal. In NIPS'07, 2008.
2. Olivier Catoni. Statistical learning theory and stochastic optimization. Springer-Verlag, 2004.
3. Arnak Dalalyan and Joseph Salmon.Optimal aggregation of affine estimators. In COLT'01, 2011.
4. L.K. Jones. A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training. Ann. Statist., 20(1):608-613, 1992.
5. Anatoli Juditsky, Philippe Rigollet, and Alexandre Tsybakov. Learning by mirror averaging. The Annals of Statistics, 36:2183-2206, 2008.
6. Gilbert Leung and A.R. Barron. Information theory and mixing least-squares regressions. Information Theory, IEEE Transactions on, 52(8):3396-3410, aug. 2006.
7. Philippe Rigollet. Kullback-leibler aggregation and misspecified generalized linear models. arXiv:0911.2919, November 2010.
8. Pilippe Rigollet and Alexandre Tsybakov. Exponential Screening and optimal rates of sparse estimation. The Annals of Statistics, 39:731-771, 2011.
9. Yuhong Yang. Adaptive regression by mixing. Journal of the American Statistical Association, 96:574-588, 2001.