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Wednesday, June 29 • 1:54pm - 2:12pm
Approximate inference in R: A case study with GLMMs and glmmsr

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ASA Grant Award Recipient

The use of realistic statistical models for complex data is often hindered by the high cost of conducting inference about the model parameters. Because of this, it is sometimes necessary to use approximate inference methods, even though the impact of these approximations on the fitted model might not be well understood. I will discuss some practical examples of this, demonstrating how to fit various Generalized Linear Mixed Models with the R package glmmsr, using a variety of approximation methods, with a focus on what difference the choice of approximation makes to the resulting inference. I will talk about some more general issues along the way, such as how we might detect situations in which a given approximation might give unreliable inference, and the extent to which the choice of approximation method can and should be automated. I will finish by briefly reviewing some ideas about how best to share and discuss challenging models and datasets which could motivate the development of new approximation methods.

Moderators
avatar for Patricia  Martinkova

Patricia Martinkova

researcher, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
Fulbright alumna and 2013-2015 visiting research scholar with Center for Statistics and the Social Sciences and Department of Statistics, University of Washington.

Speakers
avatar for Helen Ogden

Helen Ogden

Research Fellow, University of Warwick


Wednesday June 29, 2016 1:54pm - 2:12pm
Econ 140 579 Serra Mall, Stanford, CA 94305

Attendees (79)