deconvolve-package: Deconvolution Tools for Measurement Error Problems
In TimothyHyndman/deconvolve: Deconvolution Tools for Measurement Error Problems
Description
Author(s)
References
Description
This package provides tools for performing non-parametric deconvolution on
measurement error problems. It contains functions for finding bandwidths,
deconvolved densities and non-parametric regression estimates.
Author(s)
Aurore Delaigle, Timothy Hyndman, and Tianying Wang
References
Stefanski, L. and Carroll, R.J. (1990). Deconvoluting kernel density
estimators. Statistics, 21, 2, 169-184.
Fan, J., and Truong, Y. K. (1993), Nonparametric Regression With Errors
in Variables, The Annals of Statistics. 21, 1900-1925.
Carroll, R. J., Ruppert, D., and Stefanski, L. A. (1995). Measurement Error
in Nonlinear Models: A Modern Perspective, Second Edition. Chapman Hall, New
York.
Delaigle, A. and Gijbels, I. (2002). Estimation of integrated squared density
derivatives from a contaminated sample. Journal of the Royal
Statistical Society, B, 64, 4, 869-886.
Delaigle, A. and Gijbels, I. (2004). Practical bandwidth selection in
deconvolution kernel density estimation. Computational Statistics and
Data Analysis, 45, 2, 249 - 267.
Delaigle, A. and Gijbels, I. (2007). Frequent problems in calculating
integrals and optimizing objective functions: a case study in density
deconvolution. Statistics and Computing, 17, 349-355.
Delaigle, A., Hall, P., and Meister, A. (2008). On Deconvolution with
repeated measurements. Annals of Statistics, 36, 665-685
Delaigle, A. and Hall, P. (2008). Using SIMEX for smoothing-parameter choice
in errors-in-variables problems. Journal of the American Statistical
Association, 103, 481, 280-287
Delaigle, A. and Meister, A. (2008). Density estimation with heteroscedastic
error. Bernoulli, 14, 2, 562-579.
Delaigle, A. and Hall, P. (2016). Methodology for non-parametric
deconvolution when the error distribution is unknown. Journal of the
Royal Statistical Society: Series B (Statistical Methodology), 78, 1,
231-252.
Camirand, F., Carroll, R.J., and Delaigle, A. (2018). Estimating the
distribution of episodically consumed food measured with errors.
Manuscript.
TimothyHyndman/deconvolve documentation built on May 13, 2019, 11:51 p.m.
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Description Author(s) References
Description
This package provides tools for performing non-parametric deconvolution on measurement error problems. It contains functions for finding bandwidths, deconvolved densities and non-parametric regression estimates.
Author(s)
Aurore Delaigle, Timothy Hyndman, and Tianying Wang
References
Stefanski, L. and Carroll, R.J. (1990). Deconvoluting kernel density estimators. Statistics, 21, 2, 169-184.
Fan, J., and Truong, Y. K. (1993), Nonparametric Regression With Errors in Variables, The Annals of Statistics. 21, 1900-1925.
Carroll, R. J., Ruppert, D., and Stefanski, L. A. (1995). Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition. Chapman Hall, New York.
Delaigle, A. and Gijbels, I. (2002). Estimation of integrated squared density derivatives from a contaminated sample. Journal of the Royal Statistical Society, B, 64, 4, 869-886.
Delaigle, A. and Gijbels, I. (2004). Practical bandwidth selection in deconvolution kernel density estimation. Computational Statistics and Data Analysis, 45, 2, 249 - 267.
Delaigle, A. and Gijbels, I. (2007). Frequent problems in calculating integrals and optimizing objective functions: a case study in density deconvolution. Statistics and Computing, 17, 349-355.
Delaigle, A., Hall, P., and Meister, A. (2008). On Deconvolution with repeated measurements. Annals of Statistics, 36, 665-685
Delaigle, A. and Hall, P. (2008). Using SIMEX for smoothing-parameter choice in errors-in-variables problems. Journal of the American Statistical Association, 103, 481, 280-287
Delaigle, A. and Meister, A. (2008). Density estimation with heteroscedastic error. Bernoulli, 14, 2, 562-579.
Delaigle, A. and Hall, P. (2016). Methodology for non-parametric deconvolution when the error distribution is unknown. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78, 1, 231-252.
Camirand, F., Carroll, R.J., and Delaigle, A. (2018). Estimating the distribution of episodically consumed food measured with errors. Manuscript.
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