Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Subvector Inference in Partially Identified Models with Many Moment Inequalities
Abstract:
In this work we consider bootstrap-based inference methods for functions of the parameter vector in the presence of many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. In particular this covers the case of subvector inference, such as the inference on a single component associated with a...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Inference for a directed acyclic graphical model with interventions
Abstract:
Consider an inference problem in a directed acyclic graphical model subject to unknown interventions. In this presentation, we will give conditions for multiple unknown interventions to yield an identifiable model. For inference, we identify...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Analytics at Biden for President
Abstract:
Statistics and data science play a crucial role in modern presidential campaigns. Models are used to estimate the probability that each registered voter casts a ballot,...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Distance-based summaries and modeling of evolutionary trees
Abstract:
Ranked tree shapes are mathematical objects of great importance used to model hierarchical data and evolutionary processes with applications ranging across many fields including evolutionary biology and infectious disease transmission. While...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Several structured thresholding bandit problem
Abstract:
In this talk we will discuss the thresholding bandit problem, i.e. a sequential learning setting where the learner samples sequentially K unknown distributions for T times, and aims at outputting at the end the set of distributions whose means\(\mu_k\) are above a threshold \(\tau\). We will study this problem under four structural assumptions, i.e. shape constraints: that the sequence of means is monotone, unimodal...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Non-exchangeable random partition models for microclustering
Abstract:
Many popular random partition models, such as the Chinese restaurant process and its two-parameter extension, fall in the class of exchangeable random partitions, and have found wide applicability in model-based clustering, population genetics, ecology or network analysis. While the exchangeability assumption is sensible in many cases, it has some strong implications. In particular, Kingman’s representation theorem implies that the size of the clusters...
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