continuous time
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2022 ◽  
Vol 136 ◽  
pp. 102687
Author(s):  
José Mario González-González ◽  
Miguel Ernesto Vázquez-Méndez ◽  
Ulises Diéguez-Aranda

2022 ◽  
Vol 260 ◽  
pp. 107320
Author(s):  
Hassan Awada ◽  
Simone Di Prima ◽  
Costantino Sirca ◽  
Filippo Giadrossich ◽  
Serena Marras ◽  
...  

2022 ◽  
Vol 309 ◽  
pp. 176-195
Author(s):  
Pham The Anh ◽  
Adam Czornik ◽  
Thai Son Doan ◽  
Stefan Siegmund

2022 ◽  
pp. 1471082X2110657
Author(s):  
Sina Mews ◽  
Roland Langrock ◽  
Marius Ötting ◽  
Houda Yaqine ◽  
Jost Reinecke

Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.


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