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Randomization methods for assessing data analysis results on real‐valued matrices

https://doi.org/10.1002/sam.10042
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Generalized Monte Carlo Significance Tests
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Sequential Monte Carlo p -values
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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Network Motifs: Simple Building Blocks of Complex Networks
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Randomization of real-valued matrices for assessing the significance of data mining results
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Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs
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Sampling binary contingency tables with a greedy start
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Assessing data mining results via swap randomization
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Beyond market baskets
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Pruning and summarizing the discovered associations
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Using association rules for product assortment decisions
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Empirical bayes screening for multi-item associations
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Identifying non-actionable association rules
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Approximate counting by dynamic programming
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Sequential Monte Carlo Methods for Statistical Analysis of Tables
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On Information and Sufficiency
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Testing Ecological Patterns
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Combinatorial Properties of Matrices of Zeros and Ones
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data
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