Class cover catch digraphs for latent class discovery in gene expression monitoring by DNA microarrays

DOI10.1016/S0167-9473(02)00296-7zbMath1429.62594OpenAlexW2117794244MaRDI QIDQ951958

B. Ted Clark, David J. Marchette, Jeffrey L. Solka, Carey E. Priebe

Publication date: 4 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00296-7

zbMATH Keywords

statistical geneticsrandom graphsexploratory data analysis

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Graphical methods in statistics (62A09)

Related Items (13)

A general SLLN for the one-dimensional class cover problem ⋮ Edge density of new graph types based on a random digraph family ⋮ Relative density of the random \(R\)-factor proximity catch digraph for testing spatial patterns of segregation and association ⋮ Spatial Clustering Tests Based on the Domination Number of a New Random Digraph Family ⋮ The distribution of the relative arc density of a family of interval catch digraph based on uniform data ⋮ An investigation of new graph invariants related to the domination number of random proximity catch digraphs ⋮ Extension of one-dimensional proximity regions to higher dimensions ⋮ A new family of proximity graphs: class cover catch digraphs ⋮ The distribution of the domination number of class cover catch digraphs for non-uniform one-dimensional data ⋮ Classification using proximity catch digraphs ⋮ The use of domination number of a random proximity catch digraph for testing spatial patterns of segregation and association ⋮ A new family of random graphs for testing spatial segregation ⋮ Law of large numbers for a two-dimensional class cover problem

Cites Work

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• On the distribution of the domination number for random class cover catch digraphs
• Vision : New techniques and technologies in statistics
• Classification using class cover catch digraphs
• Characterizing the scale dimension of a high-dimensional classification problem

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