YoungStatS
The blog of Young Statisticians Europe (YSE)
bayesian-statistics
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models
Marta Catalano, Antonio Lijoi and Igor Prünster
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2022-01-17
Bayesian nonparametric (BNP) models are a prominent tool for performing flexible inference with a natural quantification of uncertainty. Traditionallly, flexible inference within a homogeneous sample is performed with exchangeable models of the type \(X_1,\dots, X_n|\tilde \mu \sim T(\tilde \mu)\),…
robust-statistics
Universal estimation with Maximum Mean Discrepancy (MMD)
Pierre Alquier
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2022-01-13
This is an updated version of a blog post on RIKEN AIP Approximate Bayesian Inference team webpage: https://team-approx-bayes.github.io/blog/mmd/ […] A very old and yet very exciting problem in statistics is the definition of a universal estimator \(\hat{\theta}\). An estimation procedure…
time-series
Reconciling the Gaussian and Whittle Likelihood with an application to estimation in the frequency domain
Junho Yang and Suhasini Subba Rao
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2022-01-06
Suppose \(\{X_t: t\in \mathbb{Z}\}\) is a second order stationary time series where \(c(r) = \text{cov}(X_{t+r},X_t)\) and \(f(\omega) = \sum_{r\in\mathbb{Z}}c(r)e^{ir\omega}\) are the corresponding autocovariance and spectral density function, respectively. For notational convenience, we assume the…
webinars
Inclusion Process and Sticky Brownian Motions
2021-12-24
Inclusion Process and Sticky Brownian Motions The ninth “One World webinar” organized by YoungStatS will take place on February 9th, 2022. Inclusion process (IP) is a stochastic lattice gas where particles perform random walks subjected to mutual attraction. For the inclusion process in the…
causal-inference
Heterogeneous Treatment Effects with Instrumental Variables: A Causal Machine Learning Approach
Falco J. Bargagli Stoffi
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2021-12-06
In our forthcoming paper on Annals of Applied Statistics, we propose a new method – which we call Bayesian Causal Forest with Instrumental Variable (BCF-IV) – to interpretably discover the subgroups with the largest or smallest causal effects in an instrumental variable setting. These are many…
probability
Frozen percolation on the binary tree is nonendogenous
Balázs Ráth, Jan M. Swart, Márton Szőke, and Tamás Terpai
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2021-11-25
In frozen percolation on a graph, there is a barrier located on each edge. Initially, the barriers are closed and they are assigned i.i.d. uniformly distributed activation times. At its activation time, a barrier opens, provided it is not frozen. At a fixed set \(\Xi\) of freezing times, all…
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