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Non-Parametric Estimation in DAD

Part of the Economic Studies in Inequality, Social Exclusion and Well-Being book series (EIAP, volume 2)

Abstract

It is often useful to visualize the shapes of income distributions. There are essentially two main approaches to doing so, and a mixture of the two. The first approach uses parametric models of income distributions. These models assume that the income distribution follows a known particular functional form, but with unknown parameters. Popular examples of such functional forms include the log-normal, the Pareto, and variants of the beta or gamma distributions. The main statistical challenge is then to estimate the unknown parameters of that functional form, and to test whether a given functional form appears to estimate better the observed distribution of income than another functional form.

Keywords

Mean Square Error Kernel Function Income Distribution Calorie Intake Window Width 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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15.3 References

  1. Härdle, W. (1990): Applied Nonparametric Regression, vol. XV, Cambridge, Cambridge university press ed.zbMATHGoogle Scholar
  2. Silverman, B. (1986): Density Estimation for Statistics and Data Analysis, London: Chapman and Hall.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

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