Analysis, detection and correction of misspecified discrete time state space models

@article{Kolei2018AnalysisDA,
  title={Analysis, detection and correction of misspecified discrete time state space models},
  author={Salima El Kolei and Fr{\'e}d{\'e}ric Patras},
  journal={J. Comput. Appl. Math.},
  year={2018},
  volume={333},
  pages={200-214}
}
4 Citations

Figures and Tables from this paper

Calibrating the Mean-Reversion Parameter in the Hull-White Model Using Neural Networks
TLDR
The proposed models produce mean reversion comparable to rolling-window linear regression’s results, allowing for greater flexibility while being less sensitive to market turbulence, and indicate the suitability of depth-wise convolution.
Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
TLDR
The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.
Parametric estimation of hidden Markov models by least squares type estimation and deconvolution
TLDR
This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs) based on the theory of estimating functions and a deconvolution strategy, and provides theoretical guarantees about the performance of the resulting estimator.

References

SHOWING 1-10 OF 24 REFERENCES
A New Approach to Linear Filtering and Prediction Problems
The clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the ?stat-tran-sition? method of analysis of dynamic systems. New result
Fault Detection and Estimation in Dynamic Systems
TLDR
A statistical technique for the detection and estimation of model errors caused by failures in the system model or the measurement model is discussed, which is based on the residual characteristics of the Kalman filter.
A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons
TLDR
The proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models.
On optimal ℓ∞ to ℓ∞ filtering
Calibration of the Heston Model with Application in Derivative Pricing and Hedging
TLDR
A novel multilevel-structured global optimization procedure, called the Hybrid Stochastic Approximation Search, which includes a technique called 'Partial resampling' in order to reduce big fluctuations produced by the first level and steering the steps through the local dynamics of the optimization landscape.
Thoughts out of noise
We study the effects of additive Gaussian noise on the behaviour of a simple spatially extended system, which is locally modelled by a nonlinear two-dimensional iterated map describing neuronal
A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
I use a new technique to derive a closed-form solution for the price of a European call option on an asset with stochastic volatility. The model allows arbitrary correlation between volatility and
The Accurate Continuous-Discrete Extended Kalman Filter for Radar Tracking
TLDR
The numerical results show that all the methods can be used for practical target tracking, but the Accurate Continuous-Discrete Extended Kalman Filter is more flexible and robust.
Precision large scale air traffic surveillance using IMM/assignment estimators
TLDR
An IMM estimator with a nonlinear motion model (coordinated turn) is shown to further improve the performance during the maneuvering periods over the IMM with linear models, which are asynchronous, heterogeneous, and geographically distributed over a large area.
On particle Gibbs sampling
TLDR
This paper presents a coupling construction between two particle Gibbs updates from different starting points and shows that the coupling probability may be made arbitrarily close to one by increasing the number of particles, and extends particle Gibbs to work with lower variance resampling schemes.
...
...