Seminars in Statistics

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December 2018

Matteo Sesia (Stanford)

19 December 2018 @ 11:00 - 13:00

"New tools for reproducible variable selection with knockoffs"

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Gary L. Rosner (Johns Hopkins University)

12 December 2018 @ 12:00

"Bayesian Approaches in Regulatory Science" Abstract Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. These clinical experiments help us learn about what works clinically and what does not work. The results of clinical trials support therapeutic and policy decisions. Decision making also arises when designing clinical trials. Investigators make many decisions…

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November 2018

Eleni Matechou (University of Kent)

23 November 2018 @ 12:00

"Bayesian nonparametric modelling of phenology using capture-recapture data"

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October 2018

Eduard Belitser (Vrije Universiteit Amsterdam)

26 October 2018 @ 12:00

Robust inference for general projection structures by empirical Bayes and penalization methods We develop a general framework of projection structures and study the problem of inference on the unknown parameter within this framework by using empirical Bayes and penalization methods. The main inference problem is the uncertainty quantification, but on the way we solve the estimation and posterior contraction problems as well (also a weak version of structure recovery problem). The approach is local in that the quality of the inference procedures is measured by the local quantity,…

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Stephanie van der Pas (Leiden University)

19 October 2018 @ 12:00

Posterior concentration for Bayesian regression trees and their ensembles Since their inception in the 1980's, regression trees have been one of the more widely used nonparametric prediction methods. Tree-structured methods yield a histogram reconstruction of the regression surface, where the bins correspond to terminal nodes of recursive partitioning. Trees are powerful, yet susceptible to overfitting. Strategies against overfitting have traditionally relied on pruning greedily grown trees. The Bayesian framework offers an alternative remedy against overfitting through priors. Roughly speaking, a good prior charges…

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Fernando A. Quintana (Pontificia Universidad Catolica de Chile)

5 October 2018 @ 12:00

Discovering Interactions Using Covariate Informed Random Partition Models Combination chemotherapy treatment regimens created for patients diagnosedwith childhood acute lymphoblastic leukemia have had great success inimproving cure rates. Unfortunately, patients prescribed these types oftreatment regimens have displayed susceptibility to the onset ofosteonecrosis. Some have suggested that this is due to pharmacokineticinteraction between two agents in the treatment regimen (asparaginase anddexamethasone) and other physiological variables.  Determining whichphysiological variables to consider when searching for interactions inscenarios like these, minus a priori guidance, has…

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August 2018

Jan Naudts (Universiteit Antwerpen)

9 August 2018 @ 12:00

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 research is still developing and has applications in many domains. My interest in this domain is twofold. The notion of an exponential family of statistical…

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May 2018

Kolyan Ray (King’s College London)

4 May 2018 @ 12:00

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 causal inference. We show that the marginal posterior distribution for the mean response arising from product priors on the different model parameters satisfies a semiparametric Bernstein-von…

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April 2018

Theodore Kypraios (University of Nottingham)

27 April 2018 @ 12:00

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 demonstrated how can wedraw Bayesian inference for the model parameters using Markov ChainMonte Carlo methods as well as Approximate Bayesian Computationmethodology. Finally a real dataset…

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Brunero Liseo (Università di Roma La Sapienza)

13 April 2018 @ 12:00

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 Bayesian computational (ABC) approach for estimating the importance of the categories. We illustrate the performance of the estimation procedure on simulated datasets. Finally, we use…

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