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Non-parametric confidence bands in deconvolution density estimation
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DOI：10.1111/j.1467-9868.2007.599.x.png)
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摘要
En 中文
Uniform confidence bands for densities f via non-parametric kernel estimates were first constructed by Bickel and Rosenblatt. In this paper this is extended to confidence bands in the deconvolution problem g=f*psi for an ordinary smooth error density psi. Under certain regularity conditions, we obtain asymptotic uniform confidence bands based on the asymptotic distribution of the maximal deviation (L-infinity-distance) between a deconvolution kernel estimator (f) over cap and f. Further consistency of the simple non-parametric bootstrap is proved. For our theoretical developments the bias is simply corrected by choosing an undersmoothing bandwidth. For practical purposes we propose a new data-driven bandwidth selector that is based on heuristic arguments, which aims at minimizing the L-infinity-distance between (f) over cap and f. Although not constructed explicitly to undersmooth the estimator, a simulation study reveals that the bandwidth selector suggested performs well in finite samples, in terms of both area and coverage probability of the resulting confidence bands. Finally the methodology is applied to measurements of the metallicity of local F and G dwarf stars. Our results confirm the 'G dwarf problem', i.e. the lack of metal poor G dwarfs relative to predictions from 'closed box models' of stellar formation.
Keyword:
astrophysics
asymptotic normality
bandwidth selection
bootstrap
confidence band
deconvolution
non-parametric density estimation
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期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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