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X-WR-CALNAME:Collegio Carlo Alberto
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X-WR-CALDESC:Events for Collegio Carlo Alberto
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DTSTART:20170326T010000
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BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20200529T150000
DTEND;TZID=Europe/Rome:20200529T163000
DTSTAMP:20220529T034426
CREATED:20200506T084044Z
LAST-MODIFIED:20200909T102008Z
UID:32609-1590764400-1590769800@www.carloalberto.org
SUMMARY:Tommaso Rigon\, Duke University
DESCRIPTION:“A generalized Bayes framework for probabilistic clustering” \nAbstract: Clustering methods such as k-means and its variants are standard tools for finding groups in the data. However\, despite their huge popularity\, the underlying uncertainty can not be easily quantified. On the other hand\, mixture models represent a well-established inferential tool for probabilistic clustering\, but they are characterized by severe computational bottlenecks and may have unreliable solutions in presence of misspecifications. Instead\, we rely on a generalized Bayes framework for probabilistic clustering based on Gibbs posteriors. Broadly speaking\, in such a setting the log-likelihood is replaced by an arbitrary loss function and this arguably leads to much richer families of clustering methods. Our contribution is two-fold: first\, we describe a clustering pipeline for efficiently finding groups and then quantifying the associated uncertainty. Second\, we discuss two broad classes of loss functions which have advantages in terms of analytic tractability and interpretability. Specifically\, we consider losses based on Bregman divergences and pairwise dissimilarities and we show they can be interpreted as profile and composite log-likelihoods\, respectively. Full Bayesian inference is conducted via Gibbs sampling but efficient deterministic algorithms are available for point estimation. As an important byproduct of our work\, we show that several existing clustering approaches can be interpreted as generalized Bayesian estimators under specific loss functions. Hence\, our methodology can be also used to formally quantify the uncertainty in widely used clustering approaches. Joint work with Amy Herring (Duke University) and David Dunson (Duke University).
URL:https://www.carloalberto.org/event/webinar-tommaso-rigon-duke-university/
CATEGORIES:Seminars in Statistics,Webinars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20200522T120000
DTEND;TZID=Europe/Rome:20200522T133000
DTSTAMP:20220529T034426
CREATED:20200516T100657Z
LAST-MODIFIED:20200909T102033Z
UID:32771-1590148800-1590154200@www.carloalberto.org
SUMMARY:Pierre Jacob\, Harvard University
DESCRIPTION:“Unbiased Markov chain Monte Carlo with couplings” \nAbstract: \nVarious tasks in statistics involve numerical integration\, for which Markov chain Monte Carlo (MCMC) methods are state-of-the-art. MCMC methods yield estimators that converge to integrals of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal; first\, it stands at odds with current trends in computing hardware\, with increasingly parallel architectures; secondly\, the choice of “burn-in” or “warm-up” is arduous. This talk will describe recently proposed estimators that are unbiased for the expectations of interest while having a finite computing cost and a finite variance. They can thus be generated independently in parallel and averaged over. The method also provides practical upper bounds on the distance (e.g. total variation) between the marginal distribution of the chain at a finite step and its invariant distribution. The key idea is to generate “faithful” couplings of Markov chains\, whereby pairs of chains coalesce after a random number of iterations. This talk will provide an overview of this line of research. (joint work with John O’Leary\, Yves Atchadé) \nThe main reference has just appeared here: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12336
URL:https://www.carloalberto.org/event/webinar-pierre-jacob-harvard-university/
CATEGORIES:Seminars in Statistics,Webinars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20200515T120000
DTEND;TZID=Europe/Rome:20200515T133000
DTSTAMP:20220529T034426
CREATED:20200506T083146Z
LAST-MODIFIED:20200909T102100Z
UID:32607-1589544000-1589549400@www.carloalberto.org
SUMMARY:Ismael Castillo\, Sorbonne Université Paris
DESCRIPTION:“Multiscale analysis of Bayesian CART” \nAbstract: This work affords new insights about Bayesian CART in the context of structured wavelet shrinkage. We show that practically used Bayesian CART priors lead to adaptive rate-minimax posterior concentration in the supremum norm in Gaussian white noise\, performing optimally up to a logarithmic factor. To further explore the benefits of structured shrinkage\, we propose the g-prior for trees\, which departs from the typical wavelet product priors by harnessing correlation induced by the tree topology. Building on supremum norm adaptation\, an adaptive nonparametric Bernstein–von Mises theorem for Bayesian CART is derived using multiscale techniques. For the fundamental goal of uncertainty quantification\, we construct adaptive confidence bands with uniform coverage for the regression function under self-similarity. (Joint work with Veronika Rockova).
URL:https://www.carloalberto.org/event/webinar-ismael-castillo-sorbonne-universite-paris/
CATEGORIES:Seminars in Statistics,Webinars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20200508T120000
DTEND;TZID=Europe/Rome:20200508T133000
DTSTAMP:20220529T034426
CREATED:20200421T145449Z
LAST-MODIFIED:20200909T102227Z
UID:32452-1588939200-1588944600@www.carloalberto.org
SUMMARY:Francesco Sanna Passino\, Imperial College London
DESCRIPTION:“Bayesian estimation of the latent dimension and communities in stochastic blockmodels” \nAbstract: Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space\, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network\, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this talk\, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data\, showing promising performance for recovering latent community structure. Joint work with Professor Nick Heard (Imperial College London).
URL:https://www.carloalberto.org/event/francesco-sanna-passino-imperial-college-london/
CATEGORIES:Seminars in Statistics,Webinars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20191218T120000
DTEND;TZID=Europe/Rome:20191218T130000
DTSTAMP:20220529T034426
CREATED:20191011T123144Z
LAST-MODIFIED:20191213T110114Z
UID:30276-1576670400-1576674000@www.carloalberto.org
SUMMARY:Stefano Peluchetti (Cogent Labs Tokyo)
DESCRIPTION:“Deep neural networks and stochastic processes” \nWe review deep neural networks and their training\, and then focus on the connection between neural networks and stochastic processes. Deep neural networks at initialization correspond to prior models in function space\, and under appropriate assumptions they converge to Gaussian processes in the limit of infinite width. While this connection holds “a priori”\, it is possible to derive similar results for “a posteriori” training via gradient descent. In this talk we discuss these and related results\, and we introduce a recent line of research which generalizes established results to non-Gaussian initializations and stochastic processes arising in the limit of infinite depth.
URL:https://www.carloalberto.org/event/stefano-peluchetti-cogent-labs-tokyo/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20191218T110000
DTEND;TZID=Europe/Rome:20191218T120000
DTSTAMP:20220529T034426
CREATED:20191011T122849Z
LAST-MODIFIED:20191213T110040Z
UID:30273-1576666800-1576670400@www.carloalberto.org
SUMMARY:Daniel Kowal (Rice University)
DESCRIPTION:“Scalable Bayesian Inference and Summarization for Functional Data” \nModern scientific monitoring systems\, such as wearable and implantable devices\, commonly record data over a continuous domain at high resolutions. These functional data are high-dimensional\, strongly correlated\, and usually measured concurrently with other variables of interest. Bayesians models for functional data are particularly appealing: they accommodate multiple dependence structures\, handle missing or irregularly-spaced data\, and provide regularization via shrinkage priors. However\, these models are often complex\, computationally intensive\, and difficult to interpret. This talk will focus on two fundamental challenges for Bayesian functional data analysis: (1) constructing sufficiently flexible and scalable functional regression models and (2) extracting interpretable posterior summaries. The proposed modeling framework is nonparametric and uses an unknown functional basis to learn prominent functional features\, which are associated with scalar predictors within a regression model. A customized projection-based Gibbs sampler provides posterior inference with linear time complexity in the number of predictors\, which is empirically faster than existing frequentist and Bayesian alternatives. Using the posterior distribution\, a decision theoretic approach for Bayesian variable selection is developed\, which identifies a subset of covariates that retains nearly the predictive accuracy of the full model. The methodology is applied to actigraphy data to investigate the association between intraday physical activity and responses to a sleep questionnaire.
URL:https://www.carloalberto.org/event/dicembre-daniel-kowal-rice-university/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20191129T120000
DTEND;TZID=Europe/Rome:20191129T130000
DTSTAMP:20220529T034426
CREATED:20191011T120946Z
LAST-MODIFIED:20191122T114549Z
UID:30270-1575028800-1575032400@www.carloalberto.org
SUMMARY:Robin Ryder (Université Paris-Dauphine)
DESCRIPTION:“A Bayesian non-parametric methodology for inferring grammar complexity” \nBased on a set of strings from a language\, we wish to infer the complexity of the underlying grammar. To this end\, we develop a methodology to choose between two classes of formal grammars in the Chomsky hierarchy: simple regular grammars and more complex context-free grammars. To do so\, we introduce a probabilistic context-free grammar model in the form of a Hierarchical Dirichlet Process over rules expressed in Greibach Normal Form. In comparison to other representations\, this has the advantage of nesting the regular class within the context-free class. We consider model comparison both by exploiting this nesting\, and with Bayes’ factors. The model is fit using a Sequential Monte Carlo method\, implemented in the Birch probabilistic programming language. We apply this methodology to data collected from primates\, for which the complexity of the grammar is a key question.
URL:https://www.carloalberto.org/event/novembre-robin-ryder-paris-dauphine/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20191018T120000
DTEND;TZID=Europe/Rome:20191018T130000
DTSTAMP:20220529T034426
CREATED:20190924T145500Z
LAST-MODIFIED:20190924T150408Z
UID:30095-1571400000-1571403600@www.carloalberto.org
SUMMARY:Mark Podolskij (AARHUS University)
DESCRIPTION:“Optimal estimation of certain random quantities associated with Levy processes” \nAbstract: In this talk we present new ideas on optimality of statistical estimates of certain random quantities of stochastic processes\, such as supremum or local times. Despite the existing results on estimation of such objects through high frequency observations\, the question of optimality is rarely discussed. We will demonstrate some optimal estimation methods for the supremum and local times of the Brownian motion in the $L^2$ and $L^1$ sense. In the second part of the talk we will investigate how the main ideas can be extended towards the class of Lévy processes and continuous diffusion models.
URL:https://www.carloalberto.org/event/mark-podolskij-aarhus-university/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20190614T120000
DTEND;TZID=Europe/Rome:20190614T130000
DTSTAMP:20220529T034426
CREATED:20190221T135208Z
LAST-MODIFIED:20190607T111710Z
UID:27280-1560513600-1560517200@www.carloalberto.org
SUMMARY:Subhashis Ghoshal (North Carolina State University)
DESCRIPTION:“Posterior Contraction and Credible Sets for Filaments of Regression Functions” \nThe filament of a smooth function f consists of local maximizers of f when moving in a certain direction. The filament is an important geometrical feature of the surface of the graph of a function. It is also considered as an important lower dimensional summary in analyzing multivariate data. There have been some recent theoretical studies on estimating filaments of a density function using a nonparametric kernel density estimator. In this talk\, we consider a Bayesian approach and concentrate on the nonparametric regression problem. We study the posterior contraction rates for filaments using a finite random series of B-splines prior on the regression function. Compared with the kernel method\, this has the advantage that the bias can be better controlled when the function is smoother\, which allows obtaining better rates. Under an isotropic Holder smoothness condition\, we obtain the posterior contraction rate for the filament under two different metrics — a distance of separation along an integral curve\, and the Hausdorff distance between sets. Moreover\, we construct credible sets of optimal size for the filament with sufficient frequentist coverage. We study the performance of our proposed method through a simulation study and apply on a dataset on California earthquakes to assess the fault-line of the maximum local earthquake intensity.
URL:https://www.carloalberto.org/event/subhashis-ghosal-north-carolina-state-university/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20190510T120000
DTEND;TZID=Europe/Rome:20190510T130000
DTSTAMP:20220529T034426
CREATED:20190221T135034Z
LAST-MODIFIED:20190529T081944Z
UID:27274-1557489600-1557493200@www.carloalberto.org
SUMMARY:Francesco Stingo (Università di Firenze)
DESCRIPTION:“Statistical methods for precision medicine: prognostic and predictive modeling” \nCancer is a heterogeneous disease at different molecular\, genomic and clinical levels. Identification of prognostic and predictive biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. I will first present a prognostic model for the identification of patient-specific biomarkers based on protogenomics data; this novel Bayesian hierarchical varying-sparsity regression (BEHAVIOR) model selects clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. In the second part of the talk I will present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the (previously identified) treatment predictive and disease prognostic characteristics of a particular patient’s disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers\, and hence properly utilizes these complementary sources of information for treatment selection.
URL:https://www.carloalberto.org/event/fracesco-stingo-universita-di-firenze/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20190503T120000
DTEND;TZID=Europe/Rome:20190503T130000
DTSTAMP:20220529T034426
CREATED:20190301T095121Z
LAST-MODIFIED:20190428T204611Z
UID:27418-1556884800-1556888400@www.carloalberto.org
SUMMARY:Yosef Rinott (Hebrew University of Jerusalem)
DESCRIPTION:“Monotonicity of convergence of posteriors\, and Turan type inequalities” \nGiven a prior distribution on a space of states of nature\, suppose we quantify our belief a given state is by computing its posterior probability having received a signal. If the signal arises under the same (true) state\, does it always boost our belief that this is indeed the true state\, and when it does\, in what sense? What happens to this belief given a signal distributed according to a different state? Given a sequence of iid observations\, the posterior probability of a parameter\, when the data are generated according to the same parameter\, converges to one\, and to zero when the data are generated by another value of the parameter. We study monotonicity and unimodality properties of this convergence including stochastic orderings between prior and posterior\, and monotonicity of the expected posterior. Some of the results apply to very general settings\, and in others we focus on coin tossing. It turns out that there is a relation to Turan’s inequality for orthogonal polynomials\, and in particular\, to Legendre polynomials. \n
URL:https://www.carloalberto.org/event/yosef-rinott-hebrew-university-of-jerusalem-and-luiss-rome/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20190412T120000
DTEND;TZID=Europe/Rome:20190412T130000
DTSTAMP:20220529T034426
CREATED:20190221T134753Z
LAST-MODIFIED:20190325T090744Z
UID:27272-1555070400-1555074000@www.carloalberto.org
SUMMARY:Daniel Paulin (University of Oxford)
DESCRIPTION:“Connections between optimization and sampling” \nAbstract: In this talk\, I am going to look at some connections between optimization and sampling. In “Hamiltonian descent methods’’\, we introduce a new optimization method is based on conformal Hamiltonian dynamics. It was inspired by the literature on Hamiltonian MCMC methods. The use of general kinetic energies allows us to obtain linear rates of convergence for a much larger class than strongly convex and smooth functions. “Dual Space Preconditioning for Gradient Descent” applies a similar idea to gradient descent. We introduce a new optimization method based on nonlinear preconditioning of gradient descent\, with simple and transparent conditions for convergence. “Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-Free Convergence Rates” studies the high dimensional behaviour of the Bouncy Particle Sampler (BPS)\, a non-reversible piecewise deterministic MCMC method. Although the paths of this method are straight lines\, we show that in high dimensions they converge to a Randomised Hamiltonian Monte Carlo (RHMC) process\, whose paths are determined by the Hamiltonian dynamics. We also give a characterization of the mixing rate of the RHMC process for log-concave target distributions that can be used to tune the parameters of BPS.
URL:https://www.carloalberto.org/event/daniel-paulin-university-of-oxford/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20190301T120000
DTEND;TZID=Europe/Rome:20190301T130000
DTSTAMP:20220529T034426
CREATED:20190221T121637Z
LAST-MODIFIED:20190312T145151Z
UID:27264-1551441600-1551445200@www.carloalberto.org
SUMMARY:Jouchi Nakajima (Bank for International Settlements (BIS))
DESCRIPTION:“Effectiveness of unconventional monetary policies in a low interest rate environment” \nAbstract\nHave unconventional monetary policies (UMPs) become less effective at stimulating economies in persistently low interest rate environments? This paper examines that question with a time-varying parameter VAR for the United States\, the United Kingdom\, the euro area and Japan. One advantage of our approach is the ability to measure an economy’s evolving interest rate sensitivity during the post-GFC macroeconomy. Another advantage is the ability to capture time variation in the “natural”\, or steady state\, rate of interest\, which allows us to separate interest rate movements that are associated with changes in the stance of monetary policy from those that are not.
URL:https://www.carloalberto.org/event/jouchi-nakajima/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20181219T110000
DTEND;TZID=Europe/Rome:20181219T120000
DTSTAMP:20220529T034426
CREATED:20181128T114203Z
LAST-MODIFIED:20190312T145415Z
UID:14898-1545217200-1545220800@www.carloalberto.org
SUMMARY:Matteo Sesia (Stanford University)
DESCRIPTION:“New tools for reproducible variable selection with knockoffs”\n \nAbstract \nModel-X knockoffs [1] is a new statistical framework that allows the scientist to investigate the relationship between a response of interest and hundreds or thousands of explanatory variables. In particular\, model-X knockoffs can be used to identify a subset of important variables from a larger pool that could potentially explain a phenomenon under study while rigorously controlling the false discovery rate [2] in very complex statistical models. In this talk we will briefly review the fundamentals of knockoffs and their use in two different scenarios. First\, we will discuss about how knockoffs can be used to exploit prior knowledge of genetic variation to obtain a powerful tool for genome-wide association studies [3]. Then\, we will see how the information contained in large unsupervised datasets can be harnessed to perform effectively “model-free” variable selection [4]. \nReferences: \n[1] E. J. Candès\, Y. Fan\, L. Janson\, and J. Lv\, “Panning for gold: “model-X” knockoffs for high dimensional controlled variable selection”. Journal of the Royal Statistical Society: Series B (Statistical Methodology)\, 2018.\n[2] Y. Benjamini and Y. Hochberg\, “Controlling the false discovery rate: a practical and powerful approach to multiple testing”. Journal of the Royal Statistical Society: Series B (Statistical Methodology)\, 1995.\n[3] M. Sesia\, C. Sabatti\, and E. J. Candès\, “Gene hunting with hidden Markov model knockoffs”. Biometrika\, 2018.\n[4] Yaniv Romano\, Matteo Sesia and Emmanuel Candès\, “Deep Knockoffs”. arXiv:1811.06687\, 2018.
URL:https://www.carloalberto.org/event/matteo-sesia-stanford/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20181212T120000
DTEND;TZID=Europe/Rome:20181212T120000
DTSTAMP:20220529T034426
CREATED:20180827T163018Z
LAST-MODIFIED:20190208T111730Z
UID:8498-1544616000-1544616000@www.carloalberto.org
SUMMARY:Gary L. Rosner (Johns Hopkins University)
DESCRIPTION:“Bayesian Approaches in Regulatory Science” \nAbstract\nRegulatory 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 regarding various aspects of how they will carry out the study\, such as the primary objective of the study\, primary and secondary endpoints\, methods of analysis\, sample size\, etc. Many scientists have advocated greater application of Bayesian statistical inference in regulatory science\, and applications of Bayesian methods in drug and device development continue to increase. This talk presents a vision of the drug and device development framework and the way Bayesian inference fits naturally within it. In particular\, I advocate greater application of Bayesian decision theory in clinical evaluation of therapeutics. I present some examples that illustrate how one can use decision theory in the design of a clinical study. I also point out some of the challenges one encounters when applying decision theory to clinical research.
URL:https://www.carloalberto.org/event/gary-l-rosner-johns-hopkins-university/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20181123T120000
DTEND;TZID=Europe/Rome:20181123T120000
DTSTAMP:20220529T034426
CREATED:20180827T162400Z
LAST-MODIFIED:20181119T151745Z
UID:8509-1542974400-1542974400@www.carloalberto.org
SUMMARY:Eleni Matechou (University of Kent)
DESCRIPTION:“Bayesian nonparametric modelling of phenology using capture-recapture data”
URL:https://www.carloalberto.org/event/eleni-matechou-university-of-kent/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20181026T120000
DTEND;TZID=Europe/Rome:20181026T120000
DTSTAMP:20220529T034426
CREATED:20180827T162216Z
LAST-MODIFIED:20190429T122639Z
UID:8530-1540555200-1540555200@www.carloalberto.org
SUMMARY:Eduard Belitser (Vrije Universiteit Amsterdam)
DESCRIPTION:“Robust inference for general projection structures by empirical Bayes and penalization methods” \nWe 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\nestimation 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\, the oracle rate\, which is the best trade-off between the approximation error by a projection structure and the complexity of that approximating projection structure. The approach is also robust in that the stochastic part of the general framework is assumed to satisfy only certain mild condition\, the errors may be non-iid with unknown distribution. We introduce the excessive bias restriction (EBR) under which we establish the local (oracle) confidence optimality of the constructed confidence ball. As the proposed general framework unifies a very broad class of high-dimensional models interesting and important on their own right\, the obtained general results deliver a whole avenue of results (many new ones and some known in the literature) for particular models and structures as consequences\, including white noise model and density estimation with smoothness structure\, linear regression and dictionary learning with sparsity structures\, biclustering and stochastic block models with clustering structure\, covariance matrix estimation with banding and sparsity structures\, and many others. Many adaptive minimax results over various scales follow from our local results. (Joint work with N. Nurushev.)
URL:https://www.carloalberto.org/event/eduard-belitser-vrije-universiteit-amsterdam/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20181019T120000
DTEND;TZID=Europe/Rome:20181019T120000
DTSTAMP:20220529T034426
CREATED:20180827T161843Z
LAST-MODIFIED:20190429T122454Z
UID:8538-1539950400-1539950400@www.carloalberto.org
SUMMARY:Stephanie van der Pas (Leiden University)
DESCRIPTION:“Posterior concentration for Bayesian regression trees and their ensembles” \nSince 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 smaller trees where overfitting does not occur. In this paper\, we take a step towards understanding why/when do Bayesian trees and their ensembles not overfit. We study the speed at which the posterior concentrates around the true smooth regression function. We propose a spike-and-tree variant of the popular Bayesian CART prior and establish new theoretical results showing that regression trees (and their ensembles) (a) are capable of recovering smooth regression surfaces\, achieving optimal rates up to a log factor\, (b) can adapt to the unknown level of smoothness and (c) can perform effective dimension reduction. These results provide a piece of missing theoretical evidence explaining why Bayesian trees (and additive variants thereof) have worked so well in practice.
URL:https://www.carloalberto.org/event/stephanie-van-der-pas-leiden-university/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20181005T120000
DTEND;TZID=Europe/Rome:20181005T120000
DTSTAMP:20220529T034426
CREATED:20180827T161504Z
LAST-MODIFIED:20190429T122540Z
UID:8551-1538740800-1538740800@www.carloalberto.org
SUMMARY:Fernando A. Quintana (Pontificia Universidad Catolica de Chile)
DESCRIPTION:“Discovering Interactions Using Covariate Informed Random Partition Models” \nCombination 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 proved to be achallenging problem\, particularly if interactions influence the responsedistribution in ways beyond shifts in expectation or dispersion only. We propose an exploratory technique that is able to discoverassociations between covariates and responses in a very general way. Theprocedure connects covariates to responses very flexibly through dependentrandom partition prior distributions\, and then employs machine learningtechniques to highlight potential associations found in each cluster. Weapply the method to data produced from a study dedicated to learning whichphysiological predictors influence severity of osteonecrosis multiplicatively.
URL:https://www.carloalberto.org/event/fernando-a-quintana-pontificia-universidad-catolica-de-chile/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20180809T120000
DTEND;TZID=Europe/Rome:20180809T120000
DTSTAMP:20220529T034426
CREATED:20180718T093355Z
LAST-MODIFIED:20180718T093355Z
UID:8579-1533816000-1533816000@www.carloalberto.org
SUMMARY:Jan Naudts (Universiteit Antwerpen)
DESCRIPTION:Non-Commutative Information Geometry \n \nInformation 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. \nMy interest in this domain is twofold. The notion of an exponential family of statistical models can be generalized by introducing deformed exponential functions. More recently\, I made some progress in the study of manifolds of quantum states\, more specifically\, states on a von Neumann algebra. I review what is known in the case of states on the algebra of N-times-N matrices and discuss the difficulties encountered when trying to generalize these results.
URL:https://www.carloalberto.org/event/jan-naudts-departement-fysica-universiteit-antwerpen-belgium/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20180504T120000
DTEND;TZID=Europe/Rome:20180504T120000
DTSTAMP:20220529T034426
CREATED:20180220T073131Z
LAST-MODIFIED:20181112T101521Z
UID:1554-1525435200-1525435200@www.carloalberto.org
SUMMARY:Kolyan Ray (King’s College London)
DESCRIPTION:Estimating the mean response in a missing data model \nWe 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 Mises theorem under some conditions. We also propose a more involved prior geared towards estimating this specific functional. \nThis is joint work with Aad van der Vaart.
URL:https://www.carloalberto.org/event/kolyan-ray/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20180427T120000
DTEND;TZID=Europe/Rome:20180427T120000
DTSTAMP:20220529T034426
CREATED:20180126T142045Z
LAST-MODIFIED:20181112T101525Z
UID:1558-1524830400-1524830400@www.carloalberto.org
SUMMARY:Theodore Kypraios (University of Nottingham)
DESCRIPTION:Latent Branching Trees: Modelling and Bayesian Computation. \nIn 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 on genome scheme data will be fittedto this model and we will also discuss how this kind of models can bein other settings.
URL:https://www.carloalberto.org/event/theodore-kypraios-university-of-nottingham/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20180413T120000
DTEND;TZID=Europe/Rome:20180413T120000
DTSTAMP:20220529T034426
CREATED:20180220T073006Z
LAST-MODIFIED:20181112T101632Z
UID:1567-1523620800-1523620800@www.carloalberto.org
SUMMARY:Brunero Liseo (Università di Roma La Sapienza)
DESCRIPTION:Modelling Preference Data with the Wallenius Distribution \nThe 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 the new model for analysing two datasets about movies ratings and Italian academic statisticians’ journal preferences. The latter is a novel dataset collected by the authors. \nThis is a joint work with Clara Grazian and Fabrizio Leisen.
URL:https://www.carloalberto.org/event/brunero-liseo-universita-di-roma-la-sapienza/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20180202T120000
DTEND;TZID=Europe/Rome:20180202T120000
DTSTAMP:20220529T034426
CREATED:20171001T180606Z
LAST-MODIFIED:20181112T102114Z
UID:1658-1517572800-1517572800@www.carloalberto.org
SUMMARY:Fabrizio Leisen (University of Kent)
DESCRIPTION:Compound Random Measures \nCompound 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.
URL:https://www.carloalberto.org/event/fabrizio-leisen-university-of-kent/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20180119T120000
DTEND;TZID=Europe/Rome:20180119T120000
DTSTAMP:20220529T034426
CREATED:20171107T094540Z
LAST-MODIFIED:20181112T102219Z
UID:1673-1516363200-1516363200@www.carloalberto.org
SUMMARY:Davide La Vecchia (University of Geneva)
DESCRIPTION:Saddlepoint techniques for dependent data \nSaddlepoint 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 how to fill this gap in the literature. Using the method of the tilted-Edgeworth expansion\, we devise new saddlepoint density approximations and saddlepoint test statistics in the settings of time series (short or long memory) and spatial processes (panel data models\, with fixed effects\, time-varying covariates and spatially correlated errors). We compare our new approximations to the ones obtained by standard asymptotic theory\, by Edgeworth expansion and by resampling methods. The numerical exercises illustrate that our approximations yield accuracy’s improvements\, while preserving analytical tractability.
URL:https://www.carloalberto.org/event/davide-la-vecchia-university-of-geneva/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20171220T113000
DTEND;TZID=Europe/Rome:20171220T113000
DTSTAMP:20220529T034426
CREATED:20171205T142210Z
LAST-MODIFIED:20181112T102322Z
UID:1682-1513769400-1513769400@www.carloalberto.org
SUMMARY:John Armstrong (King’s College London)
DESCRIPTION:Stochastic Differential Equations as Jets \nWe 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 give rise to a coordinate free approach to understanding SDEs and diffusions on manifolds. We will consider some applications of this approach.
URL:https://www.carloalberto.org/event/john-armstrong-king-s-college-london/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20171117T120000
DTEND;TZID=Europe/Rome:20171117T120000
DTSTAMP:20220529T034426
CREATED:20170913T074427Z
LAST-MODIFIED:20181112T102609Z
UID:1718-1510920000-1510920000@www.carloalberto.org
SUMMARY:Ester Mariucci (Humboldt-Universität zu Berlin)
DESCRIPTION:Wasserstein distances and other metrics for discretely observed Lévy processes \nWe 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 Lévy processes with possibly infinite Lévy measures and non-zero Gaussian components. A lower bound for the Wasserstein distance of order p is also presented. Furthermore\, we investigate the relation between Wasserstein distances\, total variation distance and Toscani-Fourier distance; several results connecting these metrics are discussed. \nThis is a joint work with Markus Reiß.
URL:https://www.carloalberto.org/event/ester-mariucci-humboldt-universitaet-zu-berlin/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20171110T120000
DTEND;TZID=Europe/Rome:20171110T120000
DTSTAMP:20220529T034426
CREATED:20170913T073950Z
LAST-MODIFIED:20181112T102701Z
UID:1725-1510315200-1510315200@www.carloalberto.org
SUMMARY:Krzysztof Łatuszyński (University of Warwick)
DESCRIPTION:Exact Bayesian inference for discretely observed jump-diffusions \nThe 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 exact posterior distribution of the diffusion parametersand diffusion path between observations. The approach is based onBernoulli Factory type subroutines\, and is a general alternative topseudo-marginal inference. The talk will be based onhttp://imstat.org/bjps/papers/BJPS374.pdf andhttps://arxiv.org/pdf/1707.00332.pdf and is joint work with FlavioGoncalves and Gareth Roberts.
URL:https://www.carloalberto.org/event/krzysztof-atuszy-ski-university-of-warwick/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20171013T120000
DTEND;TZID=Europe/Rome:20171013T120000
DTSTAMP:20220529T034426
CREATED:20170913T073044Z
LAST-MODIFIED:20181112T102918Z
UID:1751-1507896000-1507896000@www.carloalberto.org
SUMMARY:Kazuhiko Kakamu (Kobe University)
DESCRIPTION:How does monetary policy affect income inequality in Japan? Evidence from grouped data \nCo-author: Martin Feldkircher (Oesterreichische Nationalbank (OeNB)) \nAbstract: 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 quantities. Our results indicate different effects on income inequality depending on the monetary policy measure under consideration: A traditional rate increase decreases income inequality\, whereas a reduction of asset purchases leads to more inequality. Movements of inflation expectations and equity prices might account for theses differences.
URL:https://www.carloalberto.org/event/kazuhiko-kakamu-kobe-university/
CATEGORIES:Seminars in Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20171006T120000
DTEND;TZID=Europe/Rome:20171006T120000
DTSTAMP:20220529T034426
CREATED:20170913T070052Z
LAST-MODIFIED:20181112T102949Z
UID:1758-1507291200-1507291200@www.carloalberto.org
SUMMARY:Julien Berestycki (University of Oxford)
DESCRIPTION:Branching Brownian motion with absorption \nWhat 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 described by a universal scaling limit: the Bollthausen-Sznitman coalescent. The purpose of this talk is to present several recent results which put this prediction on a rigorous footing. The model we chose is that of a one-dimensional branching Brownian motion in which particles are absorbed at the origin. A particle’s position is interpreted as the fitness of an individual and the killing at zero correspond to the removal from the population of individuals whose fitness is too low. We assume that when a particle branches\, the offspring distribution is supercritical\, but the particles are given a drift μ towards the origin. Depending on the value of μ the process can be (sub/super)-critical. I will particularly focus on the critical case\, for which I will present results concerning the extinction time and Yaglom-type limits for the behavior of the process conditioned to survive for an unusually long time\, which both improve upon results of Kesten (1978). An important tool in the proofs of these results is the convergence of branching Brownian motion with absorption to a continuous state branching process. \nBased on joint works with N. Berestycki\, J Schweinsberg and P. Maillard\, J. Schweinsberg.
URL:https://www.carloalberto.org/event/julien-berestycky-university-of-oxford/
CATEGORIES:Seminars in Statistics
END:VEVENT
END:VCALENDAR