Network knockoffs simulate synthetic features on the topological network to control FDR in dyadic regression, outperforming data splitting and standard knockoffs in simulations and a stream barrier application.
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stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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Network knockoffs: controlling false discovery in dyadic space
Network knockoffs simulate synthetic features on the topological network to control FDR in dyadic regression, outperforming data splitting and standard knockoffs in simulations and a stream barrier application.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.