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  2. Generalized method of moments - Wikipedia

    en.wikipedia.org/wiki/Generalized_method_of_moments

    In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models.Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the data's distribution function may not be known, and therefore maximum likelihood estimation is not applicable.

  3. EM algorithm and GMM model - Wikipedia

    en.wikipedia.org/wiki/EM_Algorithm_And_GMM_Model

    The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the () can be randomly initialized. In the E-step, the algorithm tries to guess the value of () based on the parameters, while in the M-step, the algorithm updates the value of the model parameters based on the guess of () of the E-step.

  4. Generalized estimating equation - Wikipedia

    en.wikipedia.org/wiki/Generalized_estimating...

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  5. Mixture model - Wikipedia

    en.wikipedia.org/wiki/Mixture_model

    A typical finite-dimensional mixture model is a hierarchical model consisting of the following components: . N random variables that are observed, each distributed according to a mixture of K components, with the components belonging to the same parametric family of distributions (e.g., all normal, all Zipfian, etc.) but with different parameters

  6. Expectation–maximization algorithm - Wikipedia

    en.wikipedia.org/wiki/Expectation–maximization...

    The use of the log likelihood can be generalized to that of the α-log likelihood ratio. Then, the α-log likelihood ratio of the observed data can be exactly expressed as equality by using the Q-function of the α-log likelihood ratio and the α-divergence. Obtaining this Q-function is a generalized E step. Its maximization is a generalized M ...

  7. Instrumental variables estimation - Wikipedia

    en.wikipedia.org/wiki/Instrumental_variables...

    The problem was that price affected both supply and demand so that a function describing only one of the two could not be constructed directly from the observational data. Wright correctly concluded that he needed a variable that correlated with either demand or supply but not both – that is, an instrumental variable.

  8. Arellano–Bond estimator - Wikipedia

    en.wikipedia.org/wiki/Arellano–Bond_estimator

    In econometrics, the Arellano–Bond estimator is a generalized method of moments estimator used to estimate dynamic models of panel data.It was proposed in 1991 by Manuel Arellano and Stephen Bond, [1] based on the earlier work by Alok Bhargava and John Denis Sargan in 1983, for addressing certain endogeneity problems. [2]

  9. Generalized linear mixed model - Wikipedia

    en.wikipedia.org/wiki/Generalized_linear_mixed_model

    MATLAB also provides a fitglme function to fit generalized linear mixed model models. The Python Statsmodels package supports binomial and poisson implementations. [14] The Julia package MixedModels.jl provides a function called GeneralizedLinearMixedModel that fits a generalized linear mixed model to provided data. [15]