= Two-stage residual mixture fitting
Fitting a <mixture model> to <regression residuals> treats the fitted mean adjustment as fixed. Even with independent homoscedastic errors, ordinary least-squares residuals have <covariance matrix> $\sigma^2(I-H)$ and satisfy fitted linear constraints, so they are not exactly independent observations. A joint <mixture regression with shared slopes> or a bootstrap of the whole fitting process better accounts for the first-stage estimation uncertainty.
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