Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Likelihood theory for high-dimensional inference
Abstract:
In this talk we consider to what extent classical methods of inference based on the likelihood function can be extended to high-dimensional settings, and what...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression
Abstract:
Generalized linear regressions are linear combinations of many regressors that set a residual orthogonal to the regressors. These include objects that are nonparametrically estimated in generalized linear models. We give debiased machine learners of parameters of interest that depend on generalized linear...
Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
Title:
Sensitivity analysis and its neighbourhoods
Abstract:
Sensitivity analysis may assist modellers from all fields of application to improve the quality of their inference. Sensitivity Analysis is crucial both in the model construction and model interpretation phases, and is considered an important ingredient of model verification and validation.
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of...
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,...
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