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Stats Regression wls
One entry point, for a linear model whose rows are not equally trusted. WeightedLeastSquares.Fit fits a linear model
with one weight per row and returns the same OlsSummary an ordinary fit does,
at statsmodels.api.WLS parity.
A weight is how much a row is trusted, proportional to the inverse of its variance: a mean over forty observations deserves more say than a mean over three. The design, the options and the table are those of ordinary least squares, so a caller who already reads one reads the other.
MathNet.Numerics 5.0.0 exports WeightedRegression.Weighted, and like every regression entry
point in that assembly it returns the coefficients and stops — the reading
decisions/0003 recorded for
the unweighted fit holds here unchanged. Decision
0004
put the weighted table first after the robust covariances.
| Type | What it is |
|---|---|
WeightedLeastSquares |
Fits the weighted model and builds the table. |
The summary, the options and the covariance choice are OlsSummary,
OlsOptions and CovarianceType.
- Regression inference — reading the table.
- Generalized least squares — when the errors are correlated, not only unequal.
- statsmodels → .NET — what is delegated and what is not.
- Python → C# equivalence.