Publications by YoungStatS
Extrapolation to unseen domains: from theory to applications
Extrapolation to unseen domains: from theory to applications Monday, April 22nd, 2024, 8:00 PT / 11:00 ET / 17:00 CET 3rd joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: Monday, April 22nd, 2024, 8:00 PT / 11:00 ET / 17:00 CET Online, via Zoom. The registratio...
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Characterization-based approach for construction of goodness-of-fit test for Lévy distribution
Introduction The Lévy distribution, together with the Normal and Cauchy distribution, belongs to the class of stable distributions, and it is among the only three distributions for which the density can be derived in a closed form. The density function of the two-parameter Lévy distribution is expressed as follows: \[\begin{equation*} f(x; \lambd...
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Merry Christmas and Happy New Year 2024!
Dear Followers of the YoungStatS project, Dear All! It has been another intense year for our project, including 4 novel One World YoungStatS webinars, 12 blogposts from leading authors in various areas of statistics and data science, probability and econometrics, and 3 short contributions for the IMS Bulletin. In particular, we wish to thank our su...
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Locally Sparse Functional Regression
Overview In this post we present a new estimation procedure for functional linear regression useful when the regression surface – or curve – is supposed to be exactly zero within specific regions of its domain. Our approach involves regularization techniques, merging a B-spline representation of the unknown coefficient function with a peculiar ...
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Stochastic Fluid Dynamics
Stochastic Fluid Dynamics Wednesday, November 15th, 6:00 PT / 9:00 ET / 15:00 CET The study of fluid dynamics equations with white forcing is a classical topic in SPDEs and ergodic theory. Recently a new wave of interest, with a shift in focus towards transport noise, has risen, due to its connections with Stochastic Geometric Mechanics, Turbulenc...
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Non-stationary wrapped Gaussian spatial response model
Background Circular data, i.e., data defined on the unit circle, can be found in many areas of science. The unique nature of these data means that conventional methods for non-circular data are not valid for these. At the same time, advances in geographical information and global positioning systems have generated large amounts of spatial data and,...
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Linear-cost unbiased estimator for large crossed random effect models via couplings
In the following we show how it is possible to obtain parallelizable, unbiased and computationally cheap estimates of Crossed random effects models with a linear cost in the number of datapoints (and paramaters) exploiting couplings. Crossed random effects models (CREM) CREM model a continuous response variables \(Y\) as depending on the sum of un...
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Algorithmic Fairness
Algorithmic Fairness Tuesday, October 3rd, 2023, 7:30 PT / 10:30 ET / 15:30 CET 2nd joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. When & Where: Tuesday, October 3rd, 2023, 7:30 PT / 10:30 ET / 15:30 CET Online, via Zoom. The registration form is available here. Speakers:...
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Testing multiple differences via symmetric hierarchical Dirichlet processes
Testing differences: from ANOVA to BNP Detecting and quantifying differences between groups is a problem of crucial significance across various fields, often addressed by practitioners using standard analysis of variance (ANOVA). However, ANOVA is subject to several well-known limitations. It primarily detects differences only in group means, assum...
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Illustration of Graphical Gaussian Process models to analyze highly multivariate spatial data
Introduction Abundant multivariate spatial data from the natural and environmental sciences demands research on the joint distribution of multiple spatially dependent variables (Wackernagel (2013), Cressie and Wikle (2011), Banerjee and Gelfand (2014)). Here, our goal is to estimate associations over spatial locations for each variable and those am...
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