Author: YoungStatS

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Regularization by Noise for Stochastic Differential and Stochastic Partial Differential Equations The regularizing effects of noisy perturbations of differential equations is a central subject of stochastic analysis. Recent breakthroughs initiated a new wave of interest, particularly concerning non-Markovian, infinite dimensional, and rough-stochastic / Young-stochastic hybrid systems. On the webinar, selected younger scholars will present ... Read More

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Theory and Methods for Inference in Multi-armed Bandit Problems Multi-armed bandit (MAB) algorithms have been argued for decades as useful to conduct adaptively-randomized experiments. By skewing the allocation of the arms towards the more efficient or informative ones, they have the potential to enhance participants’ welfare, while resulting in a more flexible, efficient, and ... Read More

Feed: R-bloggers. Author: YoungStatS. Selection of Priors in Bayesian Structural Equation Modelling Structural equation modeling (SEM) is an important framework within the social sciences that encompasses a wide variety of statistical models. Traditionally, estimation of SEMs has relied on maximum likelihood. Unfortunately, there also exist a variety of situations in which maximum likelihood performs subpar. This led researchers to turn to alternative estimation methods, in particular, Bayesian estimation of SEMs or BSEM. However, it is currently unclear how to specify the prior distribution in order to attain the advantages of Bayesian approaches. On the webinar, selected statisticians will present their ... Read More

Feed: R-bloggers. Author: YoungStatS. Recent Advances in Approximate Bayesian Inference In approximate Bayesian computation, likelihood function is intractable and needs to be itself estimated using forward simulations of the statistical model (Beaumont et al., 2002; Marin et al., 2012; Sisson et al., 2019; Martin et al., 2020). Recent years have seen numerous advances in approximate inference methods, which have enabled Bayesian inference in increasingly challenging scenarios involving complex probabilistic models and large datasets. On the webinar, selected young statisticians will present their recent works on the topic. When & Where: Wednesday, June 25th, 7:00 PT / 10:00 EST / 16:00 ... Read More

Feed: R-bloggers. Author: YoungStatS. Recent Advancements in Applied Instrumental Variable Methods Instrumental variables (IV) is one of most important and widespread research designs in economics and statistics, as it can identify causal effects in the presence of unobserved confounding. Over the past 30 years the science of IV has advanced considerably, in part through the contributions of Nobel Laureates Joshua Angrist, Guido Imbens, and James Heckman. Recent years have brought significant advances in how IV is applied, in shift-share designs, with judge or examiner instruments, and in settings with rich or complex controls. In this webinar, selected econometricians will present ... Read More

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Overview 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)), where (tilde mu) is a random measure and (T) is a suitable transformation. Notable ... Read More

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. 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/ INTRODUCTION A very old and yet very exciting problem in statistics is the definition of a universal estimator (hat{theta}). An estimation procedure that would work all the time. Close your eyes, push the button, it works, ... Read More

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Overview Suppose ({X_t: tin mathbb{Z}}) is a second order stationary time series where (c(r) = text{cov}(X_{t+r},X_t)) and (f(omega) = sum_{rinmathbb{Z}}c(r)e^{iromega}) are the corresponding autocovariance and spectral density function, respectively. For notational convenience, we assume the time series is centered, that is (textrm{E}(X_t)=0). Our aim is to fit a parametric second-order stationary model (specified by ... Read More

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. 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 condensation regime one can extract that the scaling limit of ... Read More

Feed: R-bloggers. Author: YoungStatS. [This article was first published on YoungStatS, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Problem Setting 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 situations, ranging in complexity and importance, ... Read More