Semi-parametric estimation and prediction intervals in state space models

2019/07/18 by Yunyi Zhang, Tingting Wang, Zhang, Yunyi +3
Computer Science · Engineering · #Control Systems and Identification #FOS: Mathematics #Fault Detection and Control Systems #Statistics Theory (math.ST) #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1907.07915

openalex publication_date 2019/07/18 · openalex created_date 2020/12/21 · openalex updated_date 2026/07/28

Abstract

Literatures in state space models focus on parametric inference and prediction, which fail if the state space model is not fully specified and the maximum likelihood estimation does not work. In this paper, we assume the state transition matrix and the distribution of state noises are unknown. Under this assumption, we provide methods to consistently estimate these terms. In addition, we introduce an algorithm to construct consistent prediction intervals for state vectors and future observations. We complement the asymptotic results with several numerical experiments.

Citations

• On Gibbs sampling for state space models

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