Seminar in Statistics

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Seminar in Statistics Jan Naudts (Universiteit Antwerpen)

Non-Commutative Information Geometry   Information geometry is concerned with the study of statistical manifolds. These are differentiable manifolds consisting of probability distributions. In the param- eterized case their geometry is described by a metric tensor and a pair of dually flat connections. In the more general non-parameterized case they are Banach manifolds. This area of…

Seminar in Statistics Kolyan Ray (King’s College London)

Estimating the mean response in a missing data model We study semiparametric Bayesian estimation of the mean response in a binary regression model with missing observations. We allow some dependence between the missingness and response mechanisms, which we assume are conditionally independent given some measured covariates (i.e. unconfoundedness). This model has applications in biostatistics and…

Seminar in Statistics Theodore Kypraios (University of Nottingham)

Latent Branching Trees: Modelling and Bayesian Computation. In this talk a novel class of semi-parametric time series models will bepresented, for which we can specify in advance the marginal distributionof the observations and then build the dependence structure of theobservations around them by introducing an underlying stochastic processtermed as 'latent branching tree'. It will be…

Seminar in Statistics Brunero Liseo (Università di Roma La Sapienza)

Modelling Preference Data with the Wallenius Distribution The Wallenius distribution is a generalisation of the Hypergeometric distribution where weights are assigned to balls of different colours. This naturally defines a model for ranking categories which can be used for classification purposes. Since, in general, the resulting likelihood is not analytically available, we adopt an approximate…

Seminar in Statistics Fabrizio Leisen (University of Kent)

Compound Random Measures Compound Random Measures (CoRM's) have been recently introduced by Griffin and Leisen (2017) and represent a general and tractable class of vectors of Completely Random Measures.  This talk aims to provide an overview about CoRM's by illustrating some recent developments about their use in Bayesian nonparametrics.

Seminar in Statistics Davide La Vecchia (University of Geneva)

Saddlepoint techniques for dependent data Saddlepoint techniques provide numerically accurate, higher-order, small sample approximations to the distribution of estimators and test statistics. While a rich theory is available for saddlepoint techniques in the case of independently and identically distributed observations, only a few results have been obtained for dependent data. In this talk, we explain…

Seminar in Statistics John Armstrong (King’s College London)

Stochastic Differential Equations as Jets We explain how Ito Stochastic Differential Equations (SDEs) on manifolds may be defined using 2-jets of smooth functions. We show how this relationship can be interpreted in terms of a convergent numerical scheme. We show how jets can be used to derive graphical representations of Ito SDEs and how jets…

Seminar in Statistics Ester Mariucci (Humboldt-Universität zu Berlin)

Wasserstein distances and other metrics for discretely observed Lévy processes We present some upper bounds for the Wasserstein distance of order p between the product measures associated with the increments of Lévy processes with possibly infinite Lévy measures. As an application, we derive an upper bound for the total variation distance between the marginals of…

Seminar in Statistics Krzysztof Łatuszyński (University of Warwick)

Exact Bayesian inference for discretely observed jump-diffusions The standard approach to inference for parametric diffusion processesrelies on discretisation techniques (such as the Euler method) thatintroduce an approximation error difficult to quantify especially fordiscontinuous models, like jump-diffusions.In this talk, I will present methodology for exact inference thatavoids discretisation errors and allows to design MCMC samplerstargeting the…

Seminar in Statistics Kazuhiko Kakamu (Kobe University)

How does monetary policy affect  income inequality in Japan? Evidence from grouped data Co-author: Martin Feldkircher (Oesterreichische Nationalbank (OeNB)) Abstract: We examine the effects of monetary policy on income inequality in Japan using a novel  econometric approach that jointly estimates the Gini coefficient based on micro-level grouped data of households and the dynamics of macroeconomic…