A Class of Beta Second Kind Mixture Distributions

Adnan, Mian Arif Shams; Kiser, Humayun

doi:10.2991/jsta.d.201229.001

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Repeat Steps 2 and 3 Until Convergence1

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2022

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“…But it does find local maximum likelihood estimates. And the Curse of Dimensionality arises unless one makes very strong assumptions; for example, if one fits c multivariate normal components, then one needs to estimate cd means and d dþ 1 ð Þ=2 terms for each symmetric covariance matrix (Adnan, 2021).…”

Section: Repeat Steps 2 and 3 Until Convergencementioning

confidence: 99%

Cluster analysis: A modern statistical review

Jaeger

Banks

2022

WIREs Computational Stats

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Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the territory. Instead, it focuses on hierarchical agglomerative clustering, k-means clustering, mixture models, and then several related topics of which any cluster analysis practitioner should be aware. Even then, this review cannot do justice to the chosen topics. There is a lot of literature, and often it is somewhat ad hoc. That is generally the nature of cluster analysis-each application requires a bespoke analysis. Nonetheless, clustering has proven itself to be incredibly useful as an exploratory data analysis tool in biology, advertising, recommender systems, and genomics. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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Section: Repeat Steps 2 and 3 Until Convergencementioning

confidence: 99%