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Title of Journal: Stat Comput

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Abbravation: Statistics and Computing

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Kluwer Academic Publishers-Plenum Publishers

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1573-1375

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Clustering via nonparametric density estimation

Authors: Adelchi Azzalini Nicola Torelli
Publish Date: 2007/02/03
Volume: 17, Issue: 1, Pages: 71-80
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Abstract

Although Hartigan 1975 had already put forward the idea of connecting identification of subpopulations with regions with high density of the underlying probability distribution the actual development of methods for cluster analysis has largely shifted towards other directions for computational convenience Current computational resources allow us to reconsider this formulation and to develop clustering techniques directly in order to identify local modes of the density Given a set of observations a nonparametric estimate of the underlying density function is constructed and subsets of points with high density are formed through suitable manipulation of the associated Delaunay triangulation The method is illustrated with some numerical examples

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  1. Copula regression spline models for binary outcomes
  2. A focused information criterion for graphical models
  3. An advancement in clustering via nonparametric density estimation
  4. Sequential importance sampling of binary sequences
  5. Deletion diagnostics for marginal mean and correlation model parameters in estimating equations
  6. Boosted coefficient models
  7. Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
  8. Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification
  9. Gaussian proposal density using moment matching in SMC methods
  10. Bayesian inference for Heston-STAR models
  11. Parsimonious Gaussian mixture models
  12. Efficient estimation of variance components in nonparametric mixed-effects models with large samples
  13. On the computation of the noncentral F and noncentral beta distribution
  14. A new variable importance measure for random forests with missing data
  15. Complement to a comparative analysis of heuristics for the  p -median problem
  16. Likelihood-free parallel tempering
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