Goodness-of-Fit Tests and Model Validity

By C. Huber-Carol. 2012

168 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning (2019, background): “…whether the difference between a sample of mean-shifted class accuracy from pruned and non-pruned models is “real” amounts to determining whether two data samples are drawn from the same underlying distribution, which is the subject of a large body of goodness of ﬁt literature [14, 1, 36].”
Testing exponentiality based on characterizations of the exponential distribution (2011, background): “[2], Li and Papadopoulos [3], Thode [4], Henze and Meintanis [5,6], Henze et al.”
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality (2019, background): “The primary focus of this paper is in performing a goodness-of-fit (GoF) test [16, 29] for p(x;θ).”
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality (2019, background): “The general problem of determining if a data set is drawn from a speciﬁc distribution is known as a goodness-of-ﬁt (GoF) test [13, 20].”