Mixed Effects Modeling

Description

A mixed effects model, like many other statistical models, infers the relationship between dependent (or response ) and independent variables. It generalizes the standard regression model by allowing correlation among observations, which may be due to grouping of subjects (e.g. students in different schools), or to repeated measurements made sequentially on the
same subject (e.g. longitudinal data in biostatistics or panel data in econometrics). A mixed effects model contains fixed effects and random effects components.

See reference [1] for a requirements doc for mixed effects modeling tailored to MADlib.

References

[1] Mixed effects modeling reqts doc authored by Pivotal data science team
(attached)