A non-iterative (trivial) method for posterior inference in stochastic volatility models

Tsionas, Efthymios G.

doi:10.1016/j.spl.2017.02.035

Cited by 2 publications

(1 citation statement)

References 9 publications

(12 reference statements)

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“…In recent years, the applications of the IBF algorithm appear prosperous, to name a few, Yang and Yuan [22,23] applied the idea of IBF to deal with quantile regression model and scale mixture normal regression model, respectively. Tsionas [24] used the IBF sampler to research financial areas with stochastic volatility models. As to change-point detection, Tian et al [25] was the first to apply the non-iterative Bayesian method to deal with change-point problem, and the authors identified the position of change-points in a sequence of hemolytic uremic syndrome using IBF sampler.…”

Section: Introductionmentioning

confidence: 99%

Robust Procedure for Change-Point Estimation Using Quantile Regression Model with Asymmetric Laplace Distribution

Yang

2023

Symmetry

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The usual mean change-point detecting method based on normal linear regression is not robust to heavy-tailed data with potential outlying points. We propose a robust change-point estimation procedure based on a quantile regression model with asymmetric Laplace error distribution and develop a non-iterative sampling algorithm from a Bayesian perspective. The algorithm can generate independently and identically distributed samples approximately from the posterior distribution of the position of the change-point, which can be used for statistical inferences straightforwardly. The procedure combines the robustness of quantile regression and the computational efficiency of the non-iterative sampling algorithm. A simulation study is conducted to illustrate the performance of the procedure with satisfying findings, and finally, real data is analyzed to show the usefulness of the algorithm by comparison with the usual change-point detection method based on normal regression.

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Section: Introductionmentioning

confidence: 99%

Robust Procedure for Change-Point Estimation Using Quantile Regression Model with Asymmetric Laplace Distribution

Yang

2023

Symmetry

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Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm

Shan

Yang

2021

Mathematics

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Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and a set of explanatory variables from several unknown latent homogeneous groups. However, the classical EM algorithm and Gibbs sampling to deal with this model have several weak points. In this paper, a non-iterative sampling algorithm for fitting FMNR model is proposed from a Bayesian perspective. The procedure can generate independently and identically distributed samples from the posterior distributions of the parameters and produce more reliable estimations than the EM algorithm and Gibbs sampling. Simulation studies are conducted to illustrate the performance of the algorithm with supporting results. Finally, a real data is analyzed to show the usefulness of the methodology.

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A non-iterative (trivial) method for posterior inference in stochastic volatility models

Abstract: We propose a new non-iterative, very simple but accurate, Bayesian inference procedure for the stochastic volatility model. The only requirement of our approach is to solve a large, sparse linear system which we avoid by iteration.

Cited by 2 publications

References 9 publications

Robust Procedure for Change-Point Estimation Using Quantile Regression Model with Asymmetric Laplace Distribution

Robust Procedure for Change-Point Estimation Using Quantile Regression Model with Asymmetric Laplace Distribution

Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm

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