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1.4 Robust noise models

The standard approach to linear regression is to model the noise term \(\epsilon\) as having a normal distribution. From Stan’s perspective, there is nothing special about normally distributed noise. For instance, robust regression can be accommodated by giving the noise term a Student-\(t\) distribution. To code this in Stan, the sampling distribution is changed to the following.

data {
  ...
  real<lower=0> nu;
}
...
model {
  y ~ student_t(nu, alpha + beta * x, sigma);
}

The degrees of freedom constant nu is specified as data.