Mixed-effects model

Definition: Mixed-effects model is a statistical model containing both fixed effects and random effects. In contrast to fixed effects, mixed effects models contain one or more random effects to each fixed effect, which can consist of random intercepts and random slopes. Equation of a mixed-effects model is given below:y = X + Zu + y : The dependent variable or response variable being modeled X: The design matrix of fixed effects : Fixed-effect regression coefficients Z: The design matrix of random effects u: Random effects regression coefficients : The residual error or noise in the model

Alternative definition:

Synonym: General Linear mixed models

References: https://doi.org/10.1007/BF00140873

Winter, B. (2019). Statistics for linguists: An introduction using R. Routledge.

https://doi.org/10.18637/jss.v067.i01

Seltman, Howard J. “Experimental Design and Analysis.” (2018)

Related terms: High-pass filter, Band-pass filter, preprocessing, noise