Density estimation and comparison with a penalized mixture approach

Christian Schellhase and Göran Kauermann ()

Computational Statistics, 2012, vol. 27, issue 4, 757-777

Abstract: The paper presents smooth estimation of densities utilizing penalized splines. The idea is to represent the unknown density by a convex mixture of basis densities, where the weights are estimated in a penalized form. The proposed method extends the work of Komárek and Lesaffre (Comput Stat Data Anal 52(7):3441–3458, 2008 ) and allows for general density estimation. Simulations show a convincing performance in comparison to existing density estimation routines. The idea is extended to allow the density to depend on some (factorial) covariate. Assuming a binary group indicator, for instance, we can test on equality of the densities in the groups. This provides a smooth alternative to the classical Kolmogorov-Smirnov test or an Analysis of Variance and it shows stable and powerful behaviour. Copyright Springer-Verlag 2012

Keywords: Density estimation; Mixture density estimation; Penalized spline smoothing; ANOVA (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s00180-011-0289-6

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