Mixed-Effects Model

Example(s):
- Linear Mixed-Effects Model.
- …
Counter-Example(s):
- a Mixture Model.
See: Fixed Effect, Random Effect, Repeated Measures Design, Longitudinal Study.

References

(Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Mixed_model Retrieved:2015-9-20.
- A mixed model is a statistical model containing both fixed effects and random effects, that is mixed effects. These models are useful in a wide variety of disciplines in the physical, biological and social sciences.
  They are particularly useful in settings where repeated measurements are made on the same statistical units (longitudinal study), or where measurements are made on clusters of related statistical units. Because of their advantage to deal with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures ANOVA.

https://www.mathworks.com/help/stats/linear-mixed-effects-models.html
- QUOTE: Linear mixed-effects models are extensions of linear regression models for data that are collected and summarized in groups. These models describe the relationship between a response variable and independent variables, with coefficients that can vary with respect to one or more grouping variables. A mixed-effects model consists of two parts, fixed effects and random effects. Fixed-effects terms are usually the conventional linear regression part, and the random effects are associated with individual experimental units drawn at random from a population. The random effects have prior distributions whereas fixed effects do not. Mixed-effects models can represent the covariance structure related to the grouping of data by associating the common random effects to observations that have the same level of a grouping variable.