[R-sig-eco] ONLINE COURSE – Model selection and model simplification (MSMS03)

[Oliver Hooker]

ONLINE COURSE – Model selection and model simplification (MSMS03) This
course will be delivered live

https://www.prstatistics.com/course/model-selection-and-model-simplification-msms03/

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ABOUT THIS COURSE

This two day course covers the important and general topics of statistical
model building, model evaluation, model selection, model comparison, model
simplification, and model averaging. These topics are vitally important to
almost every type of statistical analysis, yet these topics are often
poorly or incompletely understood. We begin by considering the fundamental
issue of how to measure model fit and a model’s predictive performance, and
discuss a wide range of other major model fit measurement concepts like
likelihood, log likelihood, deviance, residual sums of squares etc. We then
turn to nested model comparison, particularly in general and generalized
linear models, and their mixed effects counterparts. We then consider the
key concept of out-of-sample predictive performance, and discuss
over-fitting or how excellent fits to the observed data can lead to very
poor generalization performance. As part of this discussion of
out-of-sample generalization, we introduce leave-one-out cross-validation
and Akaike Information Criterion (AIC). We then cover general concepts and
methods related to variable selection, including stepwise regression, ridge
regression, Lasso, and elastic nets. Following this, we turn to model
averaging, which is an arguably always preferable alternative to model
selection. Finally, we cover Bayesian methods of model comparison. Here, we
describe how Bayesian methods allow us to easily compare completely
distinct statistical models using a common metric. We also describe how
Bayesian methods allow us to fit all the candidate models of potential
interest, including cases were traditional methods fail.

Please email oliverhooker using prstatistics.com.

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Oliver Hooker PhD.
PR statistics

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