Add to ORKGIn general state space models, where the computational effort required in the evaluation of the full likelihood function is infeasible, we analyze the problem of static parameter estimation based on composite likelihood functions, in particular pairwise likelihood functions. We discuss consistency and efficiency properties of the estimators obtained by maximizing these functions in state space scenario, linking these properties to the characteristics of the model. We empirically compare the efficiency between maximum pairwise likelihood and maximum full likelihood estimators. We suggest the existence of a ‘best’ distance between pairs of observations, in terms of variance of the maximum pairwise likelihood estimator
To implement maximum likelihood estimation in state-space models, the log-likelihood function must b...
In the last years there has been a growing interest in proposing methods for estimating covariance f...
State-space models are a very general class of time series capable of modeling dependent observation...
In general state space models, where the computational effort required in the evaluation of the full...
In general state space models, where the computational effort required in the evaluation of the full...
We consider stationary state space models for which the stationary distribution is not known analyti...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
We consider stationary state space models for which the stationary distribution is not known analyti...
We consider stationary state space models for which the stationary distribution is not known analyti...
A composite likelihood is usually constructed by multiplying a collection of lower dimensional margi...
Maximum likelihood estimators are often of limited practical use due to the intensive computation th...
To implement maximum likelihood estimation in state-space models, the log-likelihood function must b...
In the last years there has been a growing interest in proposing methods for estimating covariance f...
State-space models are a very general class of time series capable of modeling dependent observation...
In general state space models, where the computational effort required in the evaluation of the full...
In general state space models, where the computational effort required in the evaluation of the full...
We consider stationary state space models for which the stationary distribution is not known analyti...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
This article concerns parameter estimation for general state space models, following a frequentist l...
We consider stationary state space models for which the stationary distribution is not known analyti...
We consider stationary state space models for which the stationary distribution is not known analyti...
A composite likelihood is usually constructed by multiplying a collection of lower dimensional margi...
Maximum likelihood estimators are often of limited practical use due to the intensive computation th...
To implement maximum likelihood estimation in state-space models, the log-likelihood function must b...
In the last years there has been a growing interest in proposing methods for estimating covariance f...
State-space models are a very general class of time series capable of modeling dependent observation...