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Found 7 papers, 0 papers with code
Biological species delimitation based on genetic and spatial dissimilarity: a comparative study
The delimitation of biological species, i. e., deciding which individuals belong to the same species and whether and how many different species are represented in a data set, is key to the conservation of biodiversity.
Nonparametric consistency for maximum likelihood estimation and clustering based on mixtures of elliptically-symmetric distributions
The 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.
Some issues in robust clustering
Some key issues in robust clustering are discussed with focus on Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outliers and clusters, the interaction between robust clustering and the estimation of the number of clusters, the essential dependence of (not only) robust clustering on tuning decisions, and shortcomings of existing measurements of cluster stability when it comes to outliers.
Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response
It is shown that estimators defined by optimization, such as maximum likelihood estimators, for an unconstrained model and for parameters in the interior set of the parameter space of a constrained model are asymptotically equivalent.
Clustering with the Average Silhouette Width
The new methods prove useful and sensible in many cases, but some weaknesses are also highlighted.
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation
The intuitions are that smoothing parameters of smoothing splines reflect inverse signal-to-noise ratios and that applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close.
Recovering the number of clusters in data sets with noise features using feature rescaling factors
In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features.
