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In the linear regression model the points (x,y) have the following relationship (in the most simple example) > y_t = x_t + e_t where e_t is some error term. W
by Rainymood 9y ago
In the linear regression model the points (x,y) have the following relationship (in the most simple example)
> y_t = x_t + e_t
where e_t is some error term. Would this be a statistical or probabilistic model?
We do know that the best predictor is just the conditional expectation (in a linear setting bla bla)
> y_pred = E[x_t + e_t | .. ] = x_t
Or is this what you mean with "model"? The predictor? Sorry for being a bit confused.
- in9 9y agoUsually, every statistical model is a special case of a probabilistic model in which its parameters have been estimated by data. The regression E[Y|X] is basically the mean of a gaussian distribution of Y given X with sigma set to the error term. The whole gaussian distribution part is the probabilistic model. But to estimate wich parameters make up this model in a particular application (together with how to check if it is valid or not) is the statistical modeling part.