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Estimating treatment effects from observational spatial data using a difference-in-differences approach
For most problems involving some kind of spatial exposure, e.g. to a neighborhood environment, it is either unethical, impractical, or impossible to randomize people to different neighborhoods. So, what happens when high- or low-risk individuals are overrepresented in one group vs.
Jon Zelner
Last updated on Feb 10, 2022
How does residential segregation impact infection risk? An interactive exploration
In this tutorial, we’re going to walk through ways in which patterns of residential segregation may impact infectious disease risk through two different pathways, contact and susceptibility, and how that is likely to impact spatial and socioeconomic patterns of infection incidence.
Jon Zelner
Last updated on Feb 10, 2022
Likelihood and model fit: A visual tour
Likelihood is a concept that underlies most statistical modeling that falls under the heading of generalized linear model or GLMs. When we fit any kind of statistical model to a dataset, the goal is to find solutions that either maximize the likelihood of the data, given the model (under a frequentist, maximum likelihood estimation framework), or maximize the likelihood of the data given the data and some prior information on the value of the parameters (under a more Bayesian framework).
Jon Zelner
Last updated on Feb 10, 2022
Smoothing! An interactive tutorial approach to univariate and spatial interpolation
In this tutorial, we will introduce some key concepts and tools for smoothing and visualizing potentially non-linear data. We will focus on local regression techniques for continuous outcomes, e.g. BMI, blood pressure, etc, in in one dimension, e.
Jon Zelner
Last updated on Feb 10, 2022
Using simulation to understand frequentist confidence intervals and Bayesian credible intervals as tools for inference
In this example, we’re going to go back to basics, and use both a formula and simulation to calculate confidence intervals for a sample mean. So, first, pick a mean and standard deviation and number of samples to draw from a Normal distribution.
Jon Zelner
Last updated on Feb 10, 2022
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