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Large-sample results for optimization-based clustering methods
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf01896986openalex-snapshot
Parameter modification for clustering criteria
inferredcites →https://doi.org/10.1007/bf01901672openalex-snapshot
On characterizing optimization-based clustering methods
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On some significance tests in cluster analysis
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Investigation of proportional link linkage clustering methods
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On some significance tests in cluster analysis
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Classification as a tool of research
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Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability
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Asymptotic Behaviour of Classification Maximum Likelihood Estimates
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Optimization Methods of Cluster Analysis
inferredcites →https://doi.org/10.1093/biomet/71.3.651openalex-snapshot
A Note on the Proof by H. E. Daniels of the Asymptotic Efficiency of a Maximum Likelihood Estimator
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On the Asymptotic Behaviour of General Maximum Likelihood Estimates for the Nonregular Case Under Nonstandard Conditions
inferredcites →https://doi.org/10.1214/aop/1176993713openalex-snapshot
A Central Limit Theorem for $k$-Means Clustering
inferredcites →https://doi.org/10.1214/aos/1176344003openalex-snapshot
The Performance of the Likelihood Ratio Test When the Model is Incorrect
inferredcites →https://doi.org/10.1214/aos/1176344071openalex-snapshot
Asymptotic Distributions for Clustering Criteria
inferredcites →https://doi.org/10.1214/aos/1176345339openalex-snapshot
Strong Consistency of $K$-Means Clustering
inferredcites →https://doi.org/10.1214/aos/1176351052openalex-snapshot
Asymptotic Behavior of Statistical Estimators and of Optimal Solutions of Stochastic Optimization Problems
inferredcites →https://doi.org/10.2307/1912004openalex-snapshot
Maximum Likelihood Estimation of Misspecified Models
inferredcites →https://doi.org/10.2307/1912526openalex-snapshot
Maximum Likelihood Estimation of Misspecified Models
inferredcites →https://doi.org/10.2307/2529003openalex-snapshot
Clustering Methods Based on Likelihood Ratio Criteria
inferredcites →https://doi.org/10.2307/2529563openalex-snapshot
389: Separating Mixtures of Normal Distributions
inferredcites →https://doi.org/10.2307/2530520openalex-snapshot
Clustering Criteria and Multivariate Normal Mixtures
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Robust clustering based on trimming
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Probabilistic Aspects in Classification
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An entropy criterion for assessing the number of clusters in a mixture model
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A Hierarchical Bayesian Procedure for Two-Mode Cluster Analysis
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E-ReMI: Extended Maximal Interaction Two-mode Clustering
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Parameters not empirically identifiable or distinguishable, including correlation between Gaussian observations
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Fuzzy clustering to estimate the parameters of block mixture models
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An online classification EM algorithm based on the mixture model
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Exploring the number of groups in robust model-based clustering
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Weighted likelihood mixture modeling and model-based clustering
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Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models
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A review of robust clustering methods
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A fuzzy approach to robust regression clustering
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Robust model-based clustering with mild and gross outliers
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A classification EM algorithm for clustering and two stochastic versions
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A fast algorithm for robust constrained clustering
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Robust, fuzzy, and parsimonious clustering, based on mixtures of Factor Analyzers
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Growth miracles and failures in a Markov switching classification model of growth
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Quantile-regression-based clustering for panel data
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Strong consistency of k -parameters clustering
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On consistency of the MLE under finite mixtures of location-scale distributions with a structural parameter
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A Probabilistic Clustering Model for Variables of Mixed Type
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Robust inference for parsimonious model-based clustering
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Comparison of the mixture and the classification maximum likelihood in cluster analysis
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Cluster Analysis for Large Datasets: An Effective Algorithm for Maximizing the Mixture Likelihood
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Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods
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Assessing a mixture model for clustering with the integrated completed likelihood
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Grouping Levels of Exposure with Same Observable Effects before Class Prediction in Toxicogenomics
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An empirical comparison of arc-cosine distance, generalised Fisher ratio and normalised entropy criteria for model selection
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Mixture Model Approach for Acoustic Emission Control of Cylindrical Pressure Equipment
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On the association analysis of CNV data: a fast and robust family-based association method
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Mixture modelling for cluster analysis
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Clustering mutational spectra via classification likelihood and markov chain monte carlo algorithms
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tclust: AnRPackage for a Trimming Approach to Cluster Analysis
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Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
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Model-based clustering and model selection for binned data.
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