This paper aims to discuss some problems on state space models with estimated parameters. While existing research focus on the prediction mean squared error, this work presents some results on bias propagation into forecast and filter predictions when the mean vector of the state is taking with an estimation bias, namely, non recursive analytical expression for them. In particular, it is discussed the impact of mean bias in invariant state space models
We propose a simple but general bootstrap method for estimating the Prediction Mean Square Error (PM...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
In time series analysis state space models are very popular. Often it is interesting to sequentially...
We obtain a conditional prediction mean squared error for a state space model with estimated paramet...
This paper aims to discuss some practical problems on linear state space models with estimated param...
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
Three general classes of state space models are presented, using the single source of error formulat...
The paper is devoted to the study of the gradient computation related to procedures for identifying ...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usi...
We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. ...
We consider stationary state space models for which the stationary distribution is not known analyti...
Model diagnostics for normal and non-normal state space models is based on recursive residuals which...
variable selection State space models are a widely used tool in time series analysis to deal with pr...
We propose a simple but general bootstrap method for estimating the Prediction Mean Square Error (PM...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
In time series analysis state space models are very popular. Often it is interesting to sequentially...
We obtain a conditional prediction mean squared error for a state space model with estimated paramet...
This paper aims to discuss some practical problems on linear state space models with estimated param...
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
Three general classes of state space models are presented, using the single source of error formulat...
The paper is devoted to the study of the gradient computation related to procedures for identifying ...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usi...
We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. ...
We consider stationary state space models for which the stationary distribution is not known analyti...
Model diagnostics for normal and non-normal state space models is based on recursive residuals which...
variable selection State space models are a widely used tool in time series analysis to deal with pr...
We propose a simple but general bootstrap method for estimating the Prediction Mean Square Error (PM...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
In time series analysis state space models are very popular. Often it is interesting to sequentially...