1,721,002 research outputs found
Discussion (invited) of: Multiple-systems analysis for the quantification of modern slavery: classical and bayesian approaches; by Bernard W. Silverman
No full textDiscussion (invited) of: "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification"
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Adaptive robust location-scale estimation
No full textIn this paper we present a robust estimator for location and scale param-
eters. Our estimator is adaptive in the sense that does not require any complex fine
tuning. Moreover it is fast to compute, and it shows interesting properties in empiri-
cal experiments. Here we give a brief description of the estimator and we comment
on its performance when compared to well established estimators such as the MCD
Robust model-based clustering with covariance matrix constraints
No full textThe Optimally Tuned Robust Improper Maximum Likelihood Estima- tor (OTRIMLE) for robust model-based clustering is introduced. It is based on a ML-type procedure for a pseudo model in which clusters are represented by a finite mixture of Gaussian distributions, while noise is represented with the addition of an improper constant density (ICD). The OTRIMLE requires constraints on the underly- ing covariance matrices that prevent spurious solutions. These constraints may have strong impact on the final clustering and alternative algorithms are provided with the OTRIMLE software
Robust model-based clustering using an improper constant density selected by a distance optimization for non-outliers
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Consistency for constrained maximum likelihood estimation and clustering based on mixtures of elliptically-symmetric distributions under general data generating processes
No full textThe consistency of the maximum likelihood estimator for mixtures of elliptically-symmetric distributions for estimating its population version is shown, where the underlying distribution P is nonparametric and does not necessarily belong to the class of mixtures on which the estimator is based. In a situation where P is a mixture of well enough separated but nonparametric distributions it is shown that the components of the population version of the estimator correspond to the well separated components of P. This provides some theoretical justification for the use of such estimators for cluster analysis in case that P has well separated subpopulations even if these subpopulations differ from what the mixture model assumes
Identifiability for mixtures of distributions from a location-scale family with uniforms
Full text linkIn this paper we study the indentifiability of a class of mixture models where
a finite number of one-dimensional location scale distributions is mixed with
a finite number of uniform distributions on an interval. We define identifiability
and we show that, under certain conditions, the afore-mentioned class of distributions
is identifiable
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