RATES OF CONVERGENCE FOR THE GAUSSIAN MIXTURE SIEVE
By Christopher R. Genovese, Larry A. Wasserman. 2000
146 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Mathematical Physics
View Paper: https://www.semanticscholar.org/paper/1f14d5d38858a1df2504f1abcc4c16386b657c69
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In Artificial Intelligence
- A. van der Vaart
- Subhashis Ghosal
- Judith Rousseau
- Hemant Ishwaran
- Mahmoud Zarepour
- Sonia Petrone
- Larry A. Wasserman
- Jinyong Hahn
In Mathematical Physics
- Mahmoud Zarepour
- Sonia Petrone
- A. van der Vaart
- Subhashis Ghosal
- Hemant Ishwaran
- Xiaohong Chen
- Jinyong Hahn
- Daniel A. Ackerberg
Citations in the corpus
- Asymptotic inference for mixture models by using data‐dependent priors (2000, background): “See Genovese and Wasserman (1998) and Roeder (1992) for example.”
- A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators (2012, methodology): “It follows that Neweys estimator (19) is numerically identical to (27) when a sieve least squares estimator is used for bh and b E [D (z)jxi].”
- Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities (2001, methodology): “Recently Genovese and Wasserman (2000) also obtained bounds for bracketing Hellinger entropies for normal mixtures where the scale parameter again lies between two positive numbers and as a result, computed the convergence rates of sieve MLEs. They considered only location mixtures and obtained the ”
- POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, unknown): “Zeevi and Meir (1997), Genovese and Wasserman (2000), Roeder and Wasserman (1997), and Li and Barron (1999) also obtained approximation and classical and Bayesian consistency results for mixture models.”
- DIRICHLET PRIOR SIEVES IN FINITE NORMAL MIXTURES (2002, methodology): “This was the approach used in Roeder (1992) and Genovese and Wasserman (2000) who consider mixtures of normals under mixing distributions restricted to certain classes (for example mixing distributions with compact support).”
- Consistency of Bernstein polynomial posteriors (2002, methodology): “To show that it is not too large, we use the following result which follows from theorem 2 of Genovese and Wasserman (2000).”