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The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By usin ...
The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid ''overfitting," in which resulting density approximates are non-smooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility.
This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity.
In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Publication date: 2014-04
Number of pages: 224
Subjects: Non-fiction, Business / Law, Economics, Econometrics, Economics