Add to ORKGThe autoregressive model is a mathematical model that is often used to model data in different areas of life. If the autoregressive model is matched against the data then the order and coefficients of the autoregressive model are unknown. This paper aims to estimate the order and coefficients of an autoregressive model based on data. The hierarchical Bayesian approach is used to estimate the order and coefficients of the autoregressive model. In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. The posterior distribution has a complex shape so that the Bayesian...
In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty ...
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The smooth transition autoregressive ...
Graduation date: 1980Finite order autoregressive models for time series are often\ud used for predic...
An autoregressive moving average (ARMA) is a time series model that is applied in everyday life for ...
This paper addresses the problem of the signal segmentation within a hierarchical Bayesian framework...
This paper addresses the problem of the signal segmentation within a hierarchical Bayesian framework...
A Markov chain Monte Carlo (MCMC) approach, called a reversible jump MCMC, is employed in model sele...
We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order...
In most applications, there is uncertainty about the statistical model to be considered. In this pap...
Introduction When fitting an autoregressive model to Gaussian time series data, often the correct o...
In this paper, we address the problem of sequential Bayesian model selection. This problem does not ...
We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data m...
The aim of this paper is to demonstrate the potential of the Reversible Jump Markov Chain Monte Carl...
Piecewise linear regression models are very flexible models for modeling the data. If the piecewise ...
We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the ...
In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty ...
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The smooth transition autoregressive ...
Graduation date: 1980Finite order autoregressive models for time series are often\ud used for predic...
An autoregressive moving average (ARMA) is a time series model that is applied in everyday life for ...
This paper addresses the problem of the signal segmentation within a hierarchical Bayesian framework...
This paper addresses the problem of the signal segmentation within a hierarchical Bayesian framework...
A Markov chain Monte Carlo (MCMC) approach, called a reversible jump MCMC, is employed in model sele...
We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order...
In most applications, there is uncertainty about the statistical model to be considered. In this pap...
Introduction When fitting an autoregressive model to Gaussian time series data, often the correct o...
In this paper, we address the problem of sequential Bayesian model selection. This problem does not ...
We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data m...
The aim of this paper is to demonstrate the potential of the Reversible Jump Markov Chain Monte Carl...
Piecewise linear regression models are very flexible models for modeling the data. If the piecewise ...
We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the ...
In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty ...
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The smooth transition autoregressive ...
Graduation date: 1980Finite order autoregressive models for time series are often\ud used for predic...