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The model is linear $y_i = a\cdot x_i + b + e_i,~ i = 1,2,\ldots,N $. It is given that the noise is heavy tailed. However the distribution of noise conditional on $x$ is the same for all data points. My question is that how should I model the data generating process? Should I use a Student-t distribution for the noise process? Should I use M estimator in R? Facts: 1. OLS is not to be used. 2. 2. The noise distribution can depend on $x$, but is independent across samples. 3. conditioned on the value of $x$, noise's mean is 0. More clarification: the distribution of noise at $x$, i.e., $N(x)$ is a function of $x$. However for different data samples $i=1,2,\ldots,M$, the distribution of noise samples remains the same.

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You could use iterative feasible generalized least squares.

Start by setting weights for each datapoint to 1, i.e. no weighting, and use the following algorithm:

  1. Fit a weighted regression model for each dataset using weights.
  2. Create a single dataset combining squared residuals/errors, $e_i^2$ and their respective $x$ values.
  3. Fit $e_i^2 = a\cdot x_i + b$. If the noise is zero mean, $e_i^2$ is equal to the variance of the error at $x_i$.
  4. Update your weights with the squared errors prediction model
  5. Go back to 1 until convergence.
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    $\begingroup$ For the improvement of the community, please describe negative votes. $\endgroup$ Commented Feb 24, 2017 at 17:03

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