Consistent estimation of mixture complexity

By Lancelot F. James, Carey E. Priebe, David J. Marchette. 2001

98 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e70ac1ddb49ce8bd2f8bc5104a3ce151ac5b8cfb

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

SPADES AND MIXTURE MODELS (2009, methodology): “terature on determining the number of mixture components is still developing, and we will focus on this aspect here. Recent works on the selection of the number of components (mixture complexity) are [20, 2]. A consistent selection procedure specialized to Gaussian mixtures is suggested in [20]. The”
Robust Estimation of Mixture Complexity (2006, methodology): “James et al. (2001) showed that their estimator of mixture complexity is consistent.”
Nonparametric Identification of Multivariate Mixtures (2010, background): “…to select the number of components have been proposed in a parametric setting (see Henna 1985; Leroux 1992; Lindsay and Roeder 1992; Windham and Cutler 1992; Roeder 1994; Chen and Kalbfleisch 1996; Dacunha- Castelle and Gassiat 1997, 1999; Keribin 2000; James et al. 2001; Woo and Sriram 2006).”
Order Selection in Finite Mixture Models With a Nonsmooth Penalty (2008, methodology): “…mixtures, we compared MSCAD with six methods: two information-based criteria, AIC and BIC; the Bayesian method GWCR (Ishwaran et al. 2001); the Kullback–Leibler (KL) distance method (James et al. 2001); the Hellinger distance (HD) method (Woo and Sriram 2006); and Lassoing (Xing and Rao 2008).”
Non‐parametric identification and estimation of the number of components in multivariate mixtures (2014, methodology): “Numerous methods to select the number of components have been proposed in a parametric setting (see, for example, Henna 1985; Leroux 1992; Lindsay and Roeder 1992; Windham and Cutler 1992; Roeder 1994; Chen and Kalbfleisch 1996; Dacunha-Castelle and Gassiat 1999; Keribin 2000; James et al. 2001; Woo”
Variable Selection in Finite Mixture of Regression Models (2007, background): “Jacobs, Jordan, Nowlan, and Hinton (1991) and Jiang and Tanner (1999) discussed the use of FMR models in machine learning applications, under the name mixture-ofexperts. The books by Wedel and Kamukura (2000) and Skrondal and Rabe-Hesketh (2004) among others have comprehensive reviews on the applica”