Density estimation and comparison with a penalized mixture approach

Schellhase C, Kauermann G (2012)
Computational Statistics 27(4): 757-777.

Journal Article | Published | English

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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 Komarek 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.
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Schellhase C, Kauermann G. Density estimation and comparison with a penalized mixture approach. Computational Statistics. 2012;27(4):757-777.
Schellhase, C., & Kauermann, G. (2012). Density estimation and comparison with a penalized mixture approach. Computational Statistics, 27(4), 757-777.
Schellhase, C., and Kauermann, G. (2012). Density estimation and comparison with a penalized mixture approach. Computational Statistics 27, 757-777.
Schellhase, C., & Kauermann, G., 2012. Density estimation and comparison with a penalized mixture approach. Computational Statistics, 27(4), p 757-777.
C. Schellhase and G. Kauermann, “Density estimation and comparison with a penalized mixture approach”, Computational Statistics, vol. 27, 2012, pp. 757-777.
Schellhase, C., Kauermann, G.: Density estimation and comparison with a penalized mixture approach. Computational Statistics. 27, 757-777 (2012).
Schellhase, Christian, and Kauermann, Göran. “Density estimation and comparison with a penalized mixture approach”. Computational Statistics 27.4 (2012): 757-777.
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