We consider the problem of deriving formal objective priors for the causal/stationary autoregressive model of order p. We compare the frequentist behaviour of the most common default priors, namely the uniform (over the stationarity region) prior, the Jeffreys' prior and the reference prior
In this paper, we study the unidirectional causal links between two series in a multivariate autoreg...
Jarocinski, Marek, and Mackowiak, Bartosz, (2017) “Granger-Causal-Priority and Choice of Variables i...
In the presented work vector autoregression (VAR) models of finite order are examined. The main part...
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Baye...
Stationarity is a very common assumption in time series analysis. A vector autoregressive process is...
Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but econ...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...
We propose and illustrate a novel approach for deriving reference priors when the statistical model...
We propose and illustrate a novel approach for deriving reference priors when the statistical model...
In the thesis, the PC prior framework is applied to construct the prior distributions for dependenci...
<div><p>For longitudinal data, the modeling of a correlation matrix <b> R</b> can be a difficult sta...
<p>We propose a class of prior distributions that discipline the long-run behavior of vector autoreg...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
Abstract. We propose a class of prior distributions that discipline the long-run behavior of Vector ...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
In this paper, we study the unidirectional causal links between two series in a multivariate autoreg...
Jarocinski, Marek, and Mackowiak, Bartosz, (2017) “Granger-Causal-Priority and Choice of Variables i...
In the presented work vector autoregression (VAR) models of finite order are examined. The main part...
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Baye...
Stationarity is a very common assumption in time series analysis. A vector autoregressive process is...
Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but econ...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...
We propose and illustrate a novel approach for deriving reference priors when the statistical model...
We propose and illustrate a novel approach for deriving reference priors when the statistical model...
In the thesis, the PC prior framework is applied to construct the prior distributions for dependenci...
<div><p>For longitudinal data, the modeling of a correlation matrix <b> R</b> can be a difficult sta...
<p>We propose a class of prior distributions that discipline the long-run behavior of vector autoreg...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
Abstract. We propose a class of prior distributions that discipline the long-run behavior of Vector ...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
In this paper, we study the unidirectional causal links between two series in a multivariate autoreg...
Jarocinski, Marek, and Mackowiak, Bartosz, (2017) “Granger-Causal-Priority and Choice of Variables i...
In the presented work vector autoregression (VAR) models of finite order are examined. The main part...