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The lecture also compares gmm with other clustering methods and discusses some extensions and limitations of gmm. Generalized method of moments xiang ao j 1 the method of moments the method of moments ( mom) is merely the following proposal: proposition 1. 1 examples for example, suppose the price of a randomly chosen paperback book is normally distributed. , asymptotic efficiency) will depend on the interplay of g( z, θ) and l( z, θ).
Ve445 lecture 12: gaussian mixture models ( gmm) - a pdf file that introduces the basic concepts, algorithms and applications of gmm, a probabilistic model that assumes the data are generated from a mixture of several gaussian distributions. A gaussian mixture model ( gmm) is useful for modeling data that comes from one of several groups: the groups might be di erent from each other, but data points within the same group can be well- modeled by a gaussian distribution. However, some statistical properties of gmm estimators ( e. To calculate the gmm estimator, and in fact gmm estimation is particularly useful when l( z, θ) is not completely specified and only the moment condition e g( z, θo) = 0 can be assumed. 1 introduction this chapter describes generalized method of moments ( gmm) estima- tion for linear and non- linear models with applications in economics and finance. 1 ( mom) to estimate a population moment ( or a function of population moments) merely use the corresponding sample moment ( or a function of sample mo- ments). Gmm estimation was formalized by hansen ( 1982), and since has become one of the most widely used methods of estimation for models in economics and finance.
Cs229: machine learning.
