Non-Parametric Estimation in DAD

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


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.


Mean Square Error Kernel Function Income Distribution Calorie Intake Window Width 
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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

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© Springer Science+Business Media, LLC 2006

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