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  2. Multilevel modeling for repeated measures - Wikipedia

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

    MLM Allows Hierarchical Structure: MLM can be used for higher-order sampling procedures, whereas RM-ANOVA is limited to examining two-level sampling procedures. In other words, MLM can look at repeated measures within subjects, within a third level of analysis etc., whereas RM-ANOVA is limited to repeated measures within subjects.

  3. Seemingly unrelated regressions - Wikipedia

    en.wikipedia.org/.../Seemingly_unrelated_regressions

    In econometrics, the seemingly unrelated regressions (SUR) [1]: 306 [2]: 279 [3]: 332 or seemingly unrelated regression equations (SURE) [4] [5]: 2 model, proposed by Arnold Zellner in (1962), is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially ...

  4. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    Another way to analyze hierarchical data would be through a random-coefficients model. This model assumes that each group has a different regression model—with its own intercept and slope. [5] Because groups are sampled, the model assumes that the intercepts and slopes are also randomly sampled from a population of group intercepts and slopes.

  5. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...

  6. Multilevel regression with poststratification - Wikipedia

    en.wikipedia.org/wiki/Multilevel_regression_with...

    The multilevel regression is the use of a multilevel model to smooth noisy estimates in the cells with too little data by using overall or nearby averages. One application is estimating preferences in sub-regions (e.g., states, individual constituencies) based on individual-level survey data gathered at other levels of aggregation (e.g ...

  7. Random effects model - Wikipedia

    en.wikipedia.org/wiki/Random_effects_model

    In econometrics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables.It is a kind of hierarchical linear model, which assumes that the data being analysed are drawn from a hierarchy of different populations whose differences relate to that hierarchy.

  8. Fixed effects model - Wikipedia

    en.wikipedia.org/wiki/Fixed_effects_model

    In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities. This is in contrast to random effects models and mixed models in which all or some of the model parameters are random variables.

  9. Spike-and-slab regression - Wikipedia

    en.wikipedia.org/wiki/Spike-and-slab_regression

    A draw of γ from its prior distribution is a list of the variables included in the regression. Conditional on this set of selected variables, we take a draw from the prior distribution of the regression coefficients (if γ i = 1 then β i ≠ 0 and if γ i = 0 then β i = 0). βγ denotes the subset of β for which γ i = 1.

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