ArticleJournal of statistical software2011
Deconvolution Estimation in Measurement Error Models: The R Package decon.
Article in Journal of statistical software, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Enhancing Study Design and Analysis of MR Imaging Markers Through Measurement Error Modeling.Journal of magnetic resonance imaging : JMRI · 2026Article
- A nonparametric statistical method for deconvolving densities in the analysis of proteomic data.BMC bioinformatics · 2026Article
- sleev: An R Package for Semiparametric Likelihood Estimation with Errors in Variables.Journal of open source software · 2025Article
- Calculating detection limits and uncertainty of reference-based deconvolution of whole-blood DNA methylation data.Epigenomics · 2023Article
- STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics.Statistics in medicine · 2020Article
- Conditional Density Estimation in Measurement Error Problems.Journal of multivariate analysis · 2015Article
- Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2014Article
- Regression models for group testing data with pool dilution effects.Biostatistics (Oxford, England) · 2013Article
- Generalization of the normal-exponential model: exploration of a more accurate parametrisation for the signal distribution on Illumina BeadArrays.BMC bioinformatics · 2012Article
- Article
- The effects of error magnitude and bandwidth selection for deconvolution with unknown error distribution.Journal of nonparametric statistics · 2012Article
- Testing for differentially-expressed microRNAs with errors-in-variables nonparametric regression.PloS one · 2012Article
- Normalizing bead-based microRNA expression data: a measurement error model-based approach.Bioinformatics (Oxford, England) · 2011Article
- Article
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Authors and funding
2 authors.
Funding
Abstract
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors-in-variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples.
Identifiers
21614139PMC3100171What OpenQuestion holds
Registered trials
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