Mixed model

Not to be confused with mixture model.
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Regression analysis
Models
Estimation
Background

A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects.[1][2] 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 (see also longitudinal study), or where measurements are made on clusters of related statistical units.[2] Mixed models are often preferred over traditional analysis of variance regression models because they don't rely on the independent observations assumption. Further, they have their flexibility in dealing with missing values and uneven spacing of repeated measurements.[3] The Mixed model analysis allows measurements to be explicitly modeled in a wider variety of correlation and variance-covariance avoiding biased estimations structures.

This page will discuss mainly linear mixed-effects models rather than generalized linear mixed models or nonlinear mixed-effects models.[4]

  1. ^ Baltagi, Badi H. (2008). Econometric Analysis of Panel Data (Fourth ed.). New York: Wiley. pp. 54–55. ISBN 978-0-470-51886-1.
  2. ^ a b Gomes, Dylan G.E. (20 January 2022). "Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model?". PeerJ. 10: e12794. doi:10.7717/peerj.12794. PMC 8784019. PMID 35116198.
  3. ^ Yang, Jian; Zaitlen, NA; Goddard, ME; Visscher, PM; Prince, AL (29 January 2014). "Advantages and pitfalls in the application of mixed-model association methods". Nat Genet. 46 (2): 100–106. doi:10.1038/ng.2876. PMC 3989144. PMID 24473328.
  4. ^ Seltman, Howard (2016). Experimental Design and Analysis. Vol. 1. pp. 357–378.