Calibrated FFT-based density approximations for α-stable distributions
Computational Statistics & Data Analysis
On-line Bayesian estimation of signals in symmetric α-stable noise
IEEE Transactions on Signal Processing
Maximum likelihood estimation of stable Paretian models
Mathematical and Computer Modelling: An International Journal
Finite mixture of α-stable distributions
Digital Signal Processing
Indirect estimation of α-stable stochastic volatility models
Computational Statistics & Data Analysis
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
Likelihood-free Bayesian inference for α-stable models
Computational Statistics & Data Analysis
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A novel approach for Bayesian inference in the setting of @a-stable distributions is introduced. The proposed approach resorts to a FFT of the characteristic function in order to approximate the likelihood function. The posterior distributions of the parameters are then produced via a random walk MCMC method. Contrary to the existing MCMC schemes, the proposed approach does not require auxiliary variables, and so it is less computationally expensive, especially when large sample sizes are involved. A simulation exercise highlights the empirical properties of the sampler. An application on audio noise data demonstrates how this estimation scheme performs in practical applications.