Implicit score-driven updates preserve the full observation density to deliver global stability and mean-squared-error contraction toward the pseudo-true parameter for log-concave densities in time-varying parameter models.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
Gradient-based filters achieve exponential stability independent of the data-generating process and MSE bounds under mild moments, with implicit filters needing weaker conditions than explicit ones.
citing papers explorer
-
Implicit score-driven filters for time-varying parameter models
Implicit score-driven updates preserve the full observation density to deliver global stability and mean-squared-error contraction toward the pseudo-true parameter for log-concave densities in time-varying parameter models.
-
Gradient-based filtering under misspecification: Stability and error bounds
Gradient-based filters achieve exponential stability independent of the data-generating process and MSE bounds under mild moments, with implicit filters needing weaker conditions than explicit ones.