Scott Sisson’s ABC seminar in Paris [All about that Bayes]

On the “All about that Bayes” seminar tomorrow (Tuesday 21 at 3p.m., room 42, AgroParisTech, 16 rue Claude Bernard, Paris 5ième), Scott Sisson, School of Mathematics and Statistics at UNSW, and visiting Paris-Dauphine this month, will give a talk on

Approximate posteriors and data for Bayesian inference

Abstract
For various reasons, including large datasets and complex models, approximate inference is becoming increasingly common. In this talk I will provide three vignettes of recent work. These cover a) approximate Bayesian computation for Gaussian process density estimation, b) likelihood-free Gibbs sampling, and c) MCMC for approximate (rounded) data.

what have rough paths got to do with data science?

at the centre of Bayes

Christian Robert is giving a talk in Jussieu tomorrow

My namesake Christian (Yann) Robert (CREST) is giving a seminar tomorrow in Jussieu (Université Pierre & Marie Curie, couloir 16-26, salle 209), between 2 and 3, on composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions. Here is the abstract:

We consider the family of hierarchical Archimedean copulas obtained from multivariate exponential mixture distributions through compounding, as introduced by Cossette et al. (2017). We investigate ways of determining the structure of these copulas and estimating their parameters. An agglomerative clustering technique based on the matrix of Spearman’s rhos, combined with a bootstrap procedure, is used to identify the tree structure. Parameters are estimated through a top-down composite likelihood. The validity of the approach is illustrated through two simulation studies in which the procedure is explained step by step. The composite likelihood method is also compared to the full likelihood method in a simple case where the latter is computable.

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we need to talk about statistics

Roberto Casarin’s talk at CREST tomorrow

My former student and friend Roberto Casarin (University Ca’Foscari, Venice) will talk tomorrow at the CREST Financial Econometrics seminar on

“Bayesian Markov Switching Tensor Regression for Time-varying Networks”

Time: 10:30
Date: 14 March 2019
Place: Room 3001, ENSAE, Université Paris-Saclay

Abstract : We propose a new Bayesian Markov switching regression model for multi-dimensional arrays (tensors) of binary time series. We assume a zero-inflated logit dynamics with time-varying parameters and apply it to multi-layer temporal networks. The original contribution is threefold. First, in order to avoid over-fitting we propose a parsimonious parameterisation of the model, based on a low-rank decomposition of the tensor of regression coefficients. Second, the parameters of the tensor model are driven by a hidden Markov chain, thus allowing for structural changes. The regimes are identified through prior constraints on the mixing probability of the zero-inflated model. Finally, we model the jointly dynamics of the network and of a set of variables of interest. We follow a Bayesian approach to inference, exploiting the Pólya-Gamma data augmentation scheme for logit models in order to provide an efficient Gibbs sampler for posterior approximation. We show the effectiveness of the sampler on simulated datasets of medium-big sizes, finally we apply the methodology to a real dataset of financial networks.

my [homonym] talk this afternoon at CREST [Paris-Saclay]

Christian ROBERT (Université Lyon 1) « How large is the jump discontinuity in the diffusion coefficient of an Itô diffusion?”

Time: 3:30 pm – 4:30 pm
Date: 04th of March 2019
Place: Room 3105

Abstract : We consider high frequency observations from a one-dimensional diffusion process Y. We assume that the diffusion coefficient σ is continuously differentiable, but with a jump discontinuity at some levely. Such a diffusion has already been considered as a local volatility model for the underlying price of an asset, but raises several issues for pricing European options or for hedging such derivatives. We introduce kernel sign-constrained estimators of the left and right limits of σ at y, but up to constant factors. We present and discuss the asymptotic properties of these kernel estimators.  We then propose a method to evaluate these constant factors by looking for bandwiths for which the kernel estimators are stable by iteration. We finally provide an estimator of the jump discontinuity size and discuss its convergence rate.