Abstract: In this paper, we review classical and advanced methodologies for analysing within-subject functional Magnetic Resonance Imaging (fMRI) data. Such data are usually acquired during sensory or cognitive experiments that aims at stimulating the subject in the scanner and eliciting evoked...

This introduction to Bayesian statistics presents the main concepts as well as the principal reasons advocated in favour of a Bayesian modelling. We cover the various approaches to prior determination as well as the basis asymptotic arguments in favour of using Bayes estimators. The testing...

In many different fields such as hydrology, telecommunications, physics of condensed matter and finance, the gaussian model results unsatisfactory and reveals difficulties in fitting data with skewness, heavy tails and multimodality. The use of stable distributions allows for modelling skewness...

This paper deals with some computational aspects in the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing constants in the likelihood function, traditional MCMC methods cannot be applied. We propose an approach to sample...

While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures. Recent innovations like Monte Carlo Markov chain, sequential...

For numerous models, it is impossible to conduct an exact Bayesian inference. There are many cases where the derivation of the posterior distribution leads to intractable calculations (due to the fact that this generally involves intractable integrations). The Bayesian computational literature...

We empirically investigate the impact of option listing on the underlying stock efficiency by looking at the volume-volatility relation of underlying stock. We use a time- consistent bivariate VAR (Vector Autoregressive Regression) model that includes time duration between trades. This model...

Présente les principaux outils statistiques utilisés par les gestionnaires, illustrés par des QCM et des exercices corrigés : statistique descriptive, probabilités, principales distributions théoriques, induction statistique (échantillonnage et estimation), tests d'hypothèse, ajustement...

This paper studies the nonlinear adjustment between industrial production and carbon prices – coined as ‘the carbon-macroeconomy relationship’ – in the EU 27. We model carbon price returns and industrial production as nonlinear and state-dependent, with dynamics depending on the sign and...

The Ohlson model (OM) builds on the accounting-based residual income valuation (RIV) model for equity valuation then specifies a linear information model (LIM) for the time-series behavior of residual income. As a result, stock price is related through a simple linear formula to current book...