8 citations · 15 across the 4 of their papers we have counts for
12 papers
Testing model specification in approximate Bayesian computation
Andrés Ramírez-Hassan, David T. Frazier
We present a procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation. We demonstrate theoretically, and empir…
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
Atlanta Chakraborty, David J. Nott, Christopher Drovandi +2
There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified Bayesian inference meth…
Weak Identification in Discrete Choice Models
David T. Frazier, Eric Renault, Lina Zhang +1
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, w…
Optimal probabilistic forecasts: When do they work?
Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier +2
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Here…
Robust Approximate Bayesian Computation: An Adjustment Approach
David T. Frazier, Christopher Drovandi, Ruben Loaiza-Maya
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally appli…
Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach
David T. Frazier
In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, in…