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.
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A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.
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Multivariate standardized residuals via Mahalanobis distance from a learned local covariance yield asymptotic conditional coverage for conformal prediction under a derived sufficient condition on the data distribution.
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.
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Multivariate Standardized Residuals for Conformal Prediction
Multivariate standardized residuals via Mahalanobis distance from a learned local covariance yield asymptotic conditional coverage for conformal prediction under a derived sufficient condition on the data distribution.