Seminars in Statistics

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Seminars 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…

Seminars 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…

Seminars 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.

Seminars 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…

Seminars 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…

Seminars 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…

Seminars 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…

Seminars 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…

Seminars in Statistics Julien Berestycki (University of Oxford)

Branching Brownian motion with absorption What does the genealogy of a population under selection look like? This question is crucial for ecology and evolutionary biology and yet it is not fully understood. Recently, Brunet and Derrida have conjectured that for a whole class of models of such populations, we can expect the genealogy to be…

Seminars in Statistics Petros Dellaportas (University College London)

High dimensional jump processes with stochastic volatility We deal with the problem of identifying jumps in multiple financial time series using the stochastic volatility model combined with a jump process. We develop efficient MCMC algorithms to perform Bayesian inference for the parameters and the latent states of the proposed models. In the univariate case we…

Seminars in Statistics John Aston (University of Cambridge)

Functions, Manifolds and Statistical Linguistics Functional Data Analysis concerns the statistical study of curves and surfaces. An extension, Functional Object Data Analysis, looks at the statistical analysis of curves and surfaces which live in restricted spaces, such as on manifolds. One particular example of these are the covariance functions associated with the underlying curves. These…

Seminars in Statistics Maria De Iorio (University College London)

Dependent Generalised Dirichlet Process Priors We propose a novel Bayesian nonparametric process prior for modelling a collections of random discrete distributions. This process is defined by combining a Generalised Dirichlet Process with a suitable Beta regression framework that introduces dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations.…

Seminars in Statistics Stéphane Boucheron (Université Paris-Diderot)

Concentration inequalities in the infinite urn scheme for occupancy counts and the missing mass, with applications to Good-Turing estimators and adaptive statistical text compression An infinite urn scheme is defined by a probability mass function over positive integers. A random allocation consists of a sample of N independent drawings according to this probability distribution where…

Seminars in Statistics Vinayak Rao (Purdue University)

Path and parameter inference for Markov jump processes A variety of phenomena are best described using dynamical models which operate on a discrete state-space and in continuous time. The most common example is the Markov jump processes whose applications range from systems biology, genetics, computing networks and human-computer interactions. Posterior computations typically involve approximations like…

Seminars in Statistics Tamara Broderick (MIT)

Fast Quantification of Uncertainty and Robustness with Variational Bayes In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. These choices may be somewhat subjective and reasonably vary over some range. Thus, we wish to measure the sensitivity of posterior estimates to variation in these choices. While…

Seminars in Statistics Alejandro Jara (Pontificia Universidad Católica de Chile)

Bayesian nonparametric approaches for the analysis of compositional data based on Bernstein polynomials We will discuss Bayesian nonparametric procedures for density estimation and fully nonparametric regression for compositional data, that is, data supported in a multidimensional simplex. The procedures are based on modified classes of Bernstein polynomials. We show that the modified classes retain the…

Seminars in Statistics Bruno Scarpa (University of Padua)

Bayesian modelling of networks in business intelligence problems Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specific products, along with co-subscription networks encoding multiple purchasing behavior. Data are available for several agencies within the same insurance company, and our…

Seminars in Statistics Boyu Ren (Harvard T.H. Chan School of Public Health)

A Bayesian Nonparametric model for microbiome data analysis We develop a statistical model to analyse microbiome profiling data based on sequencing of genetic fingerprints in 16S ribosomal RNA. The analysis allows us to quantify the uncertainty in  ecological ordination and clustering methods commonly applied in microbiome research. In addition, it can be extended into a…

Seminars in Statistics Steven Scott (Google)

Predicting the Present with Bayesian Structural Time Series This article describes a system for short term forecasting based on an ensemble prediction that averages over different combinations of predictors. The system combines a structural time series model for the target series with regression component capturing the contributions of contemporaneous search query data. A spike-and-slab prior…

Seminars in Statistics Natesh Pillai (Harvard University)

Bayesian Factor Models in High Dimensions Sparse Bayesian factor models are routinely implemented for parsimonious dependence modeling and dimensionality reduction in high-dimensional applications. We provide theoretical understanding of such Bayesian procedures in terms of posterior convergence rates in inferring high-dimensional covariance matrices where the dimension can be larger than the sample size. We will also…