Loading Events

« All Events

  • This event has passed.

FREE SEMINAR – Model Selection And Model Simplification (MSMS03S)

6 April 2022 @ 5:00 pm - 5:30 pm

Free
FREE SEMINAR – Model Selection And Model Simplification (MSMS03S)

Event Date

Online registration has now closed, please email oliverhooker@prstatistics.com to be added to the seminar or to receive a link to the recording

Free seminar

This is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Model Selection And Model Simplification”.

Time

17:00 – 17:30 GMT

Speaker

Course Instructor Dr. Mark Andrews

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.

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
Tickets are no longer available

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

 

Course Instructor

    • Dr. Mark Andrews

    Works At
    Senior Lecturer, Psychology Department, Nottingham Trent University, England

    • Teaches
    • Free 1 day intro to r and r studio (FIRR)
    • Introduction To Statistics Using R And Rstudio (IRRS03)
    • Introduction to generalised linear models using r and rstudio (IGLM)
    • Introduction to mixed models using r and rstudio (IMMR)
    • Nonlinear regression using generalized additive models (GAMR)
    • Introduction to hidden markov and state space models (HMSS)
    • Introduction to machine learning and deep learning using r (IMDL)
    • Model selection and model simplification (MSMS)
    • Data visualization using gg plot 2 (r and rstudio) (DVGG)
    • Data wrangling using r and rstudio (DWRS)
    • Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
    • Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
    • Bayesian data analysis (BADA)
    • Bayesian approaches to regression and mixed effects models using r and brms (BARM)
    • Introduction to stan for bayesian data analysis (ISBD)
    • Introduction to unix (UNIX01)
    • Introduction to python (PYIN03)
    • Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
    • Machine learning and deep learning using python (PYML03)
    • Python for data science, machine learning, and scientific computing (PDMS02)

     

    Personal website

ResearchGate

Google Scholar

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

Details

Date:
6 April 2022
Time:
5:00 pm - 5:30 pm
Cost:
Free
Event Categories:
,

Venue

Delivered remotely (United Kingdom)
Western European Time Zone, United Kingdom + Google Map

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
Tickets are no longer available