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  2. Marginal model - Wikipedia

    en.wikipedia.org/wiki/Marginal_model

    In a typical multilevel model, there are level 1 & 2 residuals (R and U variables). The two variables form a joint distribution for the response variable ().In a marginal model, we collapse over the level 1 & 2 residuals and thus marginalize (see also conditional probability) the joint distribution into a univariate normal distribution.

  3. 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.

  4. 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 ...

  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 ...

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  7. Hierarchical generalized linear model - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_generalized...

    Hierarchical generalized linear models are used when observations come from different clusters. There are two types of estimators: fixed effect estimators and random effect estimators, corresponding to parameters in : = and in (), respectively. There are different ways to obtain parameter estimates for a hierarchical generalized linear model.

  8. Deviance information criterion - Wikipedia

    en.wikipedia.org/wiki/Deviance_information_criterion

    The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation.

  9. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...

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