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Latent Variables in Statistical Models: Mixed-Effects, Finite Mixture and more

Many statistical models used in practice utilize latent variables. Latent variables are simply variables that are unobserved. It may seem odd at first to consider fitting a statistical model when some of the data is unobserved (i.e., latent). However, these types of situations arise frequently in statistical practice. In this lecture, I will introduce the basic idea behind latent variables and illustrate the concept using longitudinal data analysis,  finite mixture models and other examples as well. I will also highlight the use of latent variables in controversies on the issue of selecting an  appropriate statistical model for analyzing your data.

Expectations: The participants should be familiar with basic statistical concepts and with regression in particular.

Date:
Monday, February 10, 2020
Time:
10:00am - 12:00pm
Location:
Lapidus Library Classroom G101
Categories:
  Data Day to Day  
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Profile photo of Fred LaPolla
Fred LaPolla

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