Stats-Regression

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• Regression inference
• Lodestar.Stats.Regression
• Performance — Lodestar.Stats.Regression

Namespaces

• Ordinary least squares — Lodestar.Stats.Regression — Two ways to fit a linear model by ordinary least squares.
• Weighted least squares — Lodestar.Stats.Regression — One entry point, for a linear model whose rows are not equally trusted.
• Generalized least squares — Lodestar.Stats.Regression — One entry point, for a linear model whose errors are correlated.
• Generalized linear models — Lodestar.Stats.Regression — One entry point, GeneralizedLinearModel.Fit: it fits a response through a link function instead of an identity one, by IRLS, and reports what a statsmodels GLM(...).fit() summary holds — the same inference table OrdinaryLeastSquares.Fit reports, fitted through a link instead of directly.
• Multinomial logit — Lodestar.Stats.Regression — One entry point, MultinomialLogit.Fit, for a response with more than two unordered categories.
• Instrumental variables — Lodestar.Stats.Regression — One entry point, InstrumentalVariables, for a regressor that is correlated with the error — measured with error, chosen by the unit being measured, or determined jointly with the response.
• Panel regression — Lodestar.Stats.Regression — One entry point, PanelRegression, for data that follows the same entities over several periods: firms by year, patients by visit, regions by month.
• Shared regression types — Lodestar.Stats.Regression — The data types more than one estimator family reads or returns: the test every family reports its χ² and F statistics as, and the lag windows the kernel covariances weight with.
• Instrumental-variables data — Lodestar.Stats.Regression.Instrumental — The data types InstrumentalVariables takes and returns.
• Panel data — Lodestar.Stats.Regression.Panel — The data types PanelRegression takes and returns.