Optimal networks for collective estimation with strategic sharing balance information sharing rates against integration efficiency, with performance maximized at intermediate average degrees for each network type and when sampling effort is inversely proportional to degree in heterogeneous cases.
Alvarez-Rodriguez, F
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The authors establish consistent and asymptotically normal estimators for parameters in composite birth-death processes via conditional likelihood under a Doob h-transform, plus a test for higher-order mechanisms.
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Optimal network structure for collective performance with strategic information sharing
Optimal networks for collective estimation with strategic sharing balance information sharing rates against integration efficiency, with performance maximized at intermediate average degrees for each network type and when sampling effort is inversely proportional to degree in heterogeneous cases.
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Likelihood-based inference for birth-death processes with composite birth mechanisms
The authors establish consistent and asymptotically normal estimators for parameters in composite birth-death processes via conditional likelihood under a Doob h-transform, plus a test for higher-order mechanisms.