ArticleJournal of nonparametric statistics2012
The effects of error magnitude and bandwidth selection for deconvolution with unknown error distribution.
Article in Journal of nonparametric statistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Conditional Density Estimation in Measurement Error Problems.Journal of multivariate analysis · 2015Article
- Generalization of the normal-exponential model: exploration of a more accurate parametrisation for the signal distribution on Illumina BeadArrays.BMC bioinformatics · 2012Article
- Testing for differentially-expressed microRNAs with errors-in-variables nonparametric regression.PloS one · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The error distribution is generally unknown in deconvolution problems with real applications. A separate independent experiment is thus often conducted to collect the additional noise data in those studies. In this paper, we study the nonparametric deconvolution estimation from a contaminated sample coupled with an additional noise sample. A ridge-based kernel deconvolution estimator is proposed and its asymptotic properties are investigated depending on the error magnitude. We then present a data-driven bandwidth selection algorithm with combining the bootstrap method and the idea of simulation extrapolation. The finite sample performance of the proposed methods and the effects of error magnitude are evaluated through simulation studies. A real data analysis for a gene Illumina BeadArray study is performed to illustrate the use of the proposed methods.
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Registered trials
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