Abstract
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation:
where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices for which the lengths are n i and can be expressed in the form:
Here, {y ji } (1 ≤ j ≤ n i ) are observations of the i-th treatment (1 ≤ i ≤ m); {b i } (1 ≤ i ≤ m) are realizations from N(0, d 2) (normal distribution with mean 0 and variance d 2).
Keywords
- Generalized Additive Mixed Models (GAMM)
- 2nd Set
- Generalized Linear Mixed Models (GLMM)
- Random Intercept Model
- Nonrandom Part
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Takezawa, K. (2014). Linear Mixed Model. In: Learning Regression Analysis by Simulation. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54321-3_6
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DOI: https://doi.org/10.1007/978-4-431-54321-3_6
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Online ISBN: 978-4-431-54321-3
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