Description Author(s) References

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.

Aurore Delaigle, Timothy Hyndman, and Tianying Wang

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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