“…for a general linear model (GLM), a single linear function is a sufficient statistic for each associated parameter…” The recently arXived paper “Likelihood-free inference in high-dimensional models“, by Kousathanas et al. (July 2015), proposes an ABC resolution of the dimensionality curse [when the dimension of the parameter and of the corresponding summary statistics] by turning […]
“…for a general linear model (GLM), a single linear function is a sufficient statistic for each associated parameter…” The recently arXived paper “Likelihood-free inference in high-dimensional models“, by Kousathanas et al. (July 2015), proposes an ABC resolution of the dimensionality curse [when the dimension of the parameter and of the corresponding summary statistics] by turning […]
In a cross-validated forum exchange, I used the code below to illustrate the working of an ABC algorithm: Hence I used the median and the mad as my summary statistics. And the outcome is rather surprising, for two reasons: the first one is that the posterior on the mean μ is much wider than […]
Nassim Nicholas Taleb recently wrote an article advocating the abandonment of the use of standard deviation and advocating the use of mean absolute deviation. Mean absolute deviation is indeed an interesting and useful measure- but there is a reason that standard deviation is important even if you do not like it: it prefers models that […] Related posts: Don’t use correlation to track prediction performance What does a generalized linear…
