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  2. Mixed model - Wikipedia

    en.wikipedia.org/wiki/Mixed_model

    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.

  3. Multilevel modeling for repeated measures - Wikipedia

    en.wikipedia.org/wiki/Multilevel_Modeling_for...

    One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time (i.e. growth curve modeling for longitudinal designs); however, it may also be used for repeated measures data in which time is not a factor.

  4. Restricted maximum likelihood - Wikipedia

    en.wikipedia.org/wiki/Restricted_maximum_likelihood

    In the case of variance component estimation, the original data set is replaced by a set of contrasts calculated from the data, and the likelihood function is calculated from the probability distribution of these contrasts, according to the model for the complete data set. In particular, REML is used as a method for fitting linear mixed models.

  5. Generalized additive model - Wikipedia

    en.wikipedia.org/wiki/Generalized_additive_model

    The SAS proc GAM also provides backfit GAMs. The recommended package in R for GAMs is mgcv, which stands for mixed GAM computational vehicle, [11] which is based on the reduced rank approach with automatic smoothing parameter selection. The SAS proc GAMPL is an alternative implementation. In Python, there is the PyGAM package, with similar ...

  6. Marketing mix modeling - Wikipedia

    en.wikipedia.org/wiki/Marketing_mix_modeling

    Marketing mix modeling (MMM) is an analytical approach that uses historic information to quantify impact of marketing activities on sales. Example information that can be used are syndicated point-of-sale data (aggregated collection of product retail sales activity across a chosen set of parameters, like category of product or geographic market) and companies’ internal data.

  7. Non-linear mixed-effects modeling software - Wikipedia

    en.wikipedia.org/wiki/Non-linear_mixed-effects...

    Nonlinear mixed-effects models are a special case of regression analysis for which a range of different software solutions are available. The statistical properties of nonlinear mixed-effects models make direct estimation by a BLUE estimator impossible. Nonlinear mixed effects models are therefore estimated according to Maximum Likelihood ...

  8. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    Bayesian research cycle using Bayesian nonlinear mixed effects model: (a) standard research cycle and (b) Bayesian-specific workflow. [19] Multilevel modeling is frequently used in diverse applications and it can be formulated by the Bayesian framework. Particularly, Bayesian nonlinear mixed-effects models have recently received significant ...

  9. Mixed-design analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Mixed-design_analysis_of...

    Andy Field (2009) [1] provided an example of a mixed-design ANOVA in which he wants to investigate whether personality or attractiveness is the most important quality for individuals seeking a partner. In his example, there is a speed dating event set up in which there are two sets of what he terms "stooge dates": a set of males and a set of ...