[PDF] Top 20 Bayesian analysis of multiple thresholds autoregressive model
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Bayesian analysis of multiple thresholds autoregressive model
... data-driven Bayesian approach to analyze of possible multiple threshold values in the TAR ...our Bayesian framework, the unknown threshold-dependent parameters are estimated using their posterior ... See full document
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Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1
... In this study, analytical and simulation research methodologies are used. In the analytical method, statistical models are developed for analysing and predicting electricity generation in Nigeria. Specifically, two ... See full document
6
Bayesian Analysis of Dynamic Multivariate Models with Multiple Structural Breaks
... for multiple structural breaks at unknown dates using the dou- ble maximum ...detecting multiple changes is a likelihood ratio test with the null of l breaks against the alternative l + 1 break points (Bai, ... See full document
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The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector Autoregressive Model
... programme. Bayesian estimation, prediction and impulse response analysis in VAR models were performed using the MCMC technique and the Gibbs sampler, as well as the use of posterior ... See full document
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Bayesian Document Generative Model with Explicit Multiple Topics
... that model pa- rameter θ learned by PMM is reasonable over the whole of ...of model parameter, estimating the mixture ratio of ...to analysis of similarity among documents with multiple ... See full document
9
Bayesian analysis of a Tobit quantile regression model
... the multiple-block Metropolis-Hastings algorithm is completed by updating each block, say sequentially in fixed order, using a Metropolis-Hastings step with the above acceptance probabilities, given the current ... See full document
20
Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds.
... of multiple thresholds per study, several methods have been proposed to synthesise results from multiple thresholds simultaneously, but most require a complete set of threshold results (Dukic ... See full document
21
Has the Business Cycle Changed in Japan? A Bayesian Analysis Based on a Markov Switching Model with Multiple Change Points
... each model. The log-likelihood of the model with three points is the highest and is very close to that of the model with two points, whereas the likelihood of the other model is far away from ... See full document
31
Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model
... the Bayesian approach to an unrestricted VAR model is also a solution to the over- fitting problem (Doan, Litterman, and Sims 1984; Litterman ...the Bayesian method offers a formal way of shrinking ... See full document
31
Economic forecasts with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn
... Although the question about what hyperparameters to use has been ad- dressed in a series of papers by, among others, Litterman and coauthors (Litter- man (1979), Doan, Litterman and Sims (1984), Litterman (1986)) and ... See full document
12
Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies
... of thresholds [11], including a Bayesian hierarchical ...bivariate model, which assumes an equal number of thresholds per ...common thresholds, used methods from survival ... See full document
15
Multiple Quantitative Trait Analysis Using Bayesian Networks
... U NDERSTANDING the behavior of complex traits in- volves modeling a web of interactions among the effects of genes, environmental conditions, and other covariates. Ignoring one or more of these factors may substantially ... See full document
9
Estimating Multiple Breaks in Nonstationary Autoregressive Models
... nonstationary multiple-break autoregressive models and uncover the key factors determining which break point will be identified ...our analysis shows that the duration of the break does not affect ... See full document
44
Multivariate random effects meta-analysis of diagnostic tests with multiple thresholds
... the model using Proc NLMIXED of ...final model is reached, one increases the number of quad- rature points until the parameter estimates do not change ... See full document
15
A Bayesian decision approach to rainfall thresholds based flood warning
... the analysis of three continuous time series: (i) the precipitation averaged over the catchment area, (ii) the mean soil moisture value, (iii) the river stage (or the discharge) in the target river ...generation ... See full document
14
Adaptive Shrinkage in Bayesian Vector Autoregressive Models
... our model with a set of competing models reveals that our approach improves upon competing models in terms of root mean square forecast errors and predictive ...our model performs in a typical real- data ... See full document
32
Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes
... several Bayesian analyses of VAR processes in the ...investigate Bayesian forecasting and impulse response analysis using unrestricted ...the analysis of these two approaches by considering ... See full document
39
Frequentist and Bayesian Analysis of Random Coefficient Autoregressive models
... We used the sample size n=100 and 500, and repeated the model fitting 500 times. Table (2.1) and Table (2.2) list various Monte Carlo summary statistics. The second and the third columns represent the sample mean ... See full document
146
Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models
... case model selection cannot be based on testing in a straightforward way because the alternative specifications are not ...a model selection procedure based on the maximum value of the likelihood function ... See full document
32
Bayesian analysis of random coefficient autoregressive models
... RCAR(1) model is narrower than that from AR(1)-GARCH(1,2) model when forecasting of the near future, ...RCAR(1) model, we have shown in Section ... See full document
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