32 citations · 73 across the 6 of their papers we have counts for
7 papers
Implicit score-driven filters for time-varying parameter models
Rutger-Jan Lange, Bram van Os, Dick van Dijk
We propose an observation-driven modeling framework that allows model parameters to vary over time through an implicit score-driven (ISD) update. The ISD update maximizes the logar…
Gradient-based filtering under misspecification: Stability and error bounds
Simon Donker van Heel, Rutger-Jan Lange, Bram van Os +1
Can stochastic gradient methods track a moving target? We study the problem of tracking multidimensional time-varying parameters under noisy observations and possible model misspec…
Expected Kullback-Leibler-based characterizations of score-driven updates
Ramon de Punder, Timo Dimitriadis, Rutger-Jan Lange
Score-driven (SD) models are a standard tool in statistics and econometrics, with applications in hundreds of published articles in the past decade. We provide an information-theor…
Short and simple introduction to Bellman filtering and smoothing
Rutger-Jan Lange
Based on Bellman's dynamic-programming principle, Lange (2024) presents an approximate method for filtering, smoothing and parameter estimation for possibly non-linear and/or non-G…
Bellman filtering and smoothing for state-space models
Rutger-Jan Lange
This paper presents a new filter for state-space models based on Bellman's dynamic-programming principle, allowing for nonlinearity, non-Gaussianity and degeneracy in the observati…
Distribution theory for Schrödinger's integral equation
Rutger-Jan Lange
Much of the literature on point interactions in quantum mechanics has focused on the differential form of Schrödinger's equation. This paper, in contrast, investigates the integral…