Nonparametric inference in multivariate mixtures

By Peter Hall, A. Neeman, Reza Pakyari, R. Elmore. 2005

97 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/43fab2b07c690a877b1e1195a99ed19cc45e7b01

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In Artificial Intelligence

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Citations in the corpus

Non‐parametric identification and estimation of the number of components in multivariate mixtures (2014, methodology): “Hall and Zhou (2003), Hall et al. (2005) and Allman et al. (2009) analysed non-parametric identifiability of component distributions and mixing proportions in model (1) under known M. (Kasahara and Shimotsu (2009) studied non-parametric identification of finite mixture dynamic discrete choice…”
mixtools: An R Package for Analyzing Mixture Models (2009, methodology): “This example was introduced by Hall, Neeman, Pakyari, and Elmore (2005) then later reused by Benaglia et al. (2009a) for comparison purposes.”
Identifiability of parameters in latent structure models with many observed variables (2008, background): “Having addressed the question of identifiability of the parameters of a mixture of products of binary variables, they can thus argue for the identifiability of the parameters of the original continuous model, as they continue to do in [24].”
Nonparametric Identification of Multivariate Mixtures (2010, background): “Hall and Zhou (2003), Hall et al. (2005), and Allman et al. (2009) analyze nonparametric identifiability of k-variate finite mixture models (1).”
Maximum smoothed likelihood for multivariate mixtures (2011, methodology): “It also yields considerably smaller mean integrated squared errors than an alternative algorithm (Hall et al., 2005) in a simulation study.”
Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models (2015, background): “[20] showed that, if r ≥ rm and m ≥ 2, both the component densities and mixing weights can be estimated √ n-consistently.”
Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices (2009, background): “Recently, by building on the seminal work by Hotz and Miller (1993), computationally attractive semiparametric estimators for structural dynamic models have been developed (Aguirregabiria and Mira (2002), Kasahara and Shimotsu (2006)), and a number of papers in empirical industrial organization have”
On strong identifiability and convergence rates of parameter estimation in finite mixtures (2016, background): “Keywords and phrases: mixture models, strong identifiability, Wasserstein distances, minimax bounds, maximum likelihood estimation.”
Non‐parametric estimation of finite mixtures from repeated measurements (2016, methodology): “These approaches are applicable more generally than are the earlier proposals of Hettmansperger and Thomas (2000) and Elmore et al. (2004), and of Hall and Zhou (2003) and Hall et al. (2005).”