578 research outputs found
Approximate maximum likelihood estimation using data-cloning ABC
A maximum likelihood methodology for a general class of models is presented,
using an approximate Bayesian computation (ABC) approach. The typical target of
ABC methods are models with intractable likelihoods, and we combine an ABC-MCMC
sampler with so-called "data cloning" for maximum likelihood estimation.
Accuracy of ABC methods relies on the use of a small threshold value for
comparing simulations from the model and observed data. The proposed
methodology shows how to use large threshold values, while the number of
data-clones is increased to ease convergence towards an approximate maximum
likelihood estimate. We show how to exploit the methodology to reduce the
number of iterations of a standard ABC-MCMC algorithm and therefore reduce the
computational effort, while obtaining reasonable point estimates. Simulation
studies show the good performance of our approach on models with intractable
likelihoods such as g-and-k distributions, stochastic differential equations
and state-space models.Comment: 25 pages. Minor revision. It includes a parametric bootstrap for the
exact MLE for the first example; includes mean bias and RMSE calculations for
the third example. Forthcoming in Computational Statistics and Data Analysi
Auto-Encoding Variational Bayes
In this thesis, we focused on recent developments in variational inference. We saw how these methods can be married with ideas from deep learning to give life to the example of variational autoencoders (VAEs), models that learn how to generate new realistic-looking data. Along the way, we provided parallels with other statistical methods.In this thesis, we focused on recent developments in variational inference. We saw how these methods can be married with ideas from deep learning to give life to the example of variational autoencoders (VAEs), models that learn how to generate new realistic-looking data. Along the way, we provided parallels with other statistical methods
Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss and T. Mikosch), 365-400. Springer-Verlag: New York. April 2009. )
The success of univariate stochastic volatility (SV) models in relation to univariate GARCH models has spurred an enormous interest in generalizations of SV models to a multivariate setting. A large number of multivariate SV (MSV) models are now available along with clearly articulated estimation recipes. Our goal in this paper is to provide the first detailed summary of the various model formulations, along with connections and differences, and discuss how the models are estimated. We aim to show that the developments and achievements in this area represent one of the great success stories of financial econometrics.
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