Introduces Net-LSW framework for multiscale time-varying dependence modeling on networks, with local partial correlation graphs and consistent estimation for nonstationary processes.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A distributed framework with trace-similarity penalty and invex relaxation achieves two-phase minimax optimal rates and sharper model-free prediction error bounds under unidentifiable parameters and heterogeneity.
citing papers explorer
-
Multiscale Dynamic Dependence Estimation over Networks
Introduces Net-LSW framework for multiscale time-varying dependence modeling on networks, with local partial correlation graphs and consistent estimation for nonstationary processes.
-
Distributed Prediction under Heterogeneity with Unidentifiable Parameter
A distributed framework with trace-similarity penalty and invex relaxation achieves two-phase minimax optimal rates and sharper model-free prediction error bounds under unidentifiable parameters and heterogeneity.