It's multivariate linear regression meaning multiple dependent variables (Y). Data A has both X (explanatory variables) and Y, but data B has only Y.
I wonder if there is any way to incorporate data A and B into the regression model.
If I remember correctly, Statistical Analysis with Missing Data by Little et al, suggested some kind of iterative approach like EM algorithm where you repeat following until convergence
- Estimate missing X based on current regression model
- Compute regression model with full X estimated in #1
But in this case, I have all columns of X matrix is missing. So my questions:
- Is the approach above still effective? That is, would including data B into the model help in any way?
- If so, how missing full columns of X can be estimated?
Update: The reason I want to add data B is because data B is application-specific data while data A is more general data. I hope to have training data that has more "weights" for application-specific input space.