Simple Marginally Noninformative Prior Distributions for Covariance Matrices

Huang, Alan; Wand, M. P.

doi:10.1214/13-ba815

Cited by 211 publications

(254 citation statements)

References 8 publications

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“…We set λ = 10, and local changes make little difference in terms of the Monte Carlo experiment below, Root Mean Squared Error (RMSE), or generalized impulse responses. Huang and Wand (2013) propose a prior for large sparse positive definite matrices where control is allowed over the standard deviations and the correlation coefficients. We use this in our study and provide more details on the Huang and Wand (2013) prior in Appendix 1.…”

Section: Priorsmentioning

confidence: 99%

Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

Assaf

Song

et al. 2018

Journal of Travel Research

115

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Increasing levels of global and regional integration have led to tourist flows between countries becoming closely linked. These links should be considered when modeling and forecasting international tourism demand within a region. This study introduces a comprehensive and accurate systematic approach to tourism demand analysis, based on a Bayesian global vector autoregressive (BGVAR) model. An empirical study of international tourist flows in nine countries in Southeast Asia demonstrates the ability of the BGVAR model to capture the spillover effects of international tourism demand in this region. The study provides clear evidence that the BGVAR model consistently outperforms three other alternative VAR model versions throughout one-to four-quarters-ahead forecasting horizons. The potential of the BGVAR model in future applications is demonstrated by its superiority in both modeling and forecasting tourism demand.

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Section: Priorsmentioning

confidence: 99%

Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

Assaf

Song

et al. 2018

Journal of Travel Research

115

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“…Recently, Huang et al (2013) proposed a hierarchical approach for the covariance matrix as shown in equation (4).…”

Section: Hierarchical Half-t Priormentioning

confidence: 99%

“…Alternative covariance matrix priors have been proposed including the scaled inverse Wishart (O'Malley and Zaslavsky 2005), a hierarchial inverse Wishart (Huang et al 2013), and a separation strategy (Barnard et al 2000). However Tokuda et al (2011) states that Even fewer analytical results are known for these families, making it even more challenging to understand precisely the properties of such distributions.…”

Section: Introductionmentioning

confidence: 99%

Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations

Ross

Haukos

Hagen

et al. 2018

The Auk

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Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we compare an inverse Wishart, scaled inverse Wishart, hierarchical inverse Wishart, and a separation strategy as possible priors for the covariance matrix. We evaluate these priors through a simulation study and application to a real data set. Generally all priors work well with the exception of the inverse Wishart when the true variance is small relative to prior mean. In this case, the posterior for the variance is biased toward larger values and the correlation is biased toward zero. This bias persists even for large sample sizes and therefore caution should be used when using the inverse Wishart prior.

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“…Refs. [12][13][14]. The covariance matrix M, and thus the likelihood, are then functions of ν, so that L(x|y, ν).…”

Section: Uncertainties On the Correlationmentioning

confidence: 99%

EFTfitter: a tool for interpreting measurements in the context of effective field theories

et al. 2016

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Over the past years, the interpretation of measurements in the context of effective field theories has attracted much attention in the field of particle physics. We present a tool for interpreting sets of measurements in such models using a Bayesian ansatz by calculating the posterior probabilities of the corresponding free parameters numerically. An example is given, in which top-quark measurements are used to constrain anomalous couplings at the W tb-vertex.

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Simple Marginally Noninformative Prior Distributions for Covariance Matrices

Cited by 211 publications

References 8 publications

Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations

EFTfitter: a tool for interpreting measurements in the context of effective field theories

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