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Non-linear data
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
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