Add to ORKGMisspecifications (i.e. errors on the parameters) of state space models lead to incorrect inference of the hidden states. This paper studies weakly nonlin-ear state space models with additive Gaussian noises and proposes a method for detecting and correcting misspecifications. The latter induce a biased estimator of the hidden state but also happen to induce correlation on innovations and other residues. This property is used to find a well-defined objective function for which an optimisation routine is applied to recover the true parameters of the model. It is argued that this method can consistently estimate the bias on the parameter. We demonstrate the algorithm on various models of increasing complexity
State estimation entails the estimation of an unobserved random closed set from (partial) observatio...
Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefo...
The particle filter is one of the most successful methods for state inference and identification of ...
Misspecifications (i.e. errors on the parameters) of state space models lead to incorrect inference ...
In this paper we develop and validate a procedure for testing against a shift in mean in the observa...
In this paper we develop and validate a procedure for testing against a shift in mean in the observa...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy...
We consider estimation under model misspecification where there is a model mismatch between the unde...
We consider estimation under model misspecification where there is a model mismatch between the unde...
This paper develops a bias compensation-based parameter and state estimation algorithm for the obser...
This paper develops a bias compensation-based parameter and state estimation algorithm for the obser...
We present algorithms for computing the weights implicitly assigned to observations when es-timating...
State estimation entails the estimation of an unobserved random closed set from (partial) observatio...
Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefo...
The particle filter is one of the most successful methods for state inference and identification of ...
Misspecifications (i.e. errors on the parameters) of state space models lead to incorrect inference ...
In this paper we develop and validate a procedure for testing against a shift in mean in the observa...
In this paper we develop and validate a procedure for testing against a shift in mean in the observa...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
We propose a framework for estimation and inference when the model may be misspecified. We rely on a...
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy...
We consider estimation under model misspecification where there is a model mismatch between the unde...
We consider estimation under model misspecification where there is a model mismatch between the unde...
This paper develops a bias compensation-based parameter and state estimation algorithm for the obser...
This paper develops a bias compensation-based parameter and state estimation algorithm for the obser...
We present algorithms for computing the weights implicitly assigned to observations when es-timating...
State estimation entails the estimation of an unobserved random closed set from (partial) observatio...
Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefo...
The particle filter is one of the most successful methods for state inference and identification of ...