
Aurore
Delaigle
Ph.D. Statistics, M.A. Statistics,Institute
of Statistics, UCLouvain
B.S. Mathematics School of
Mathematics, UCLouvain
Previous
positions
(in reverse chronological order)
Reader at the School of
Mathematics, University of Bristol
Assistant professor at the Department of
Mathematics, University of California
at San Diego
BAEF
postdoctoral fellow at the Department of
Statistics, University
of California at Davis
George W. Snedecor Award , IMS
Fellowship ,
Moran Medal ,
ARC QEII Fellowship,
Hellman Fellowship , BAEF
Fellowship
Editorial service
Associate
Editor for Annals of Statistics, JASA, JCGS, JRSS,B,
Australian and New Zealand Journal of Statistics,
Statistica Sinica.
Past: Associate Editor for the Journal of
Nonparametric Statistics and the Journal of the
Korean Statistical Society.
Planned trips/visits
I am regularly in Europe or in the US.
Contact me for more info.
Code
Individuals are free to use the codes for the
purpose academic research, provided it is properly
acknowledged. For any other use, permission must first
be arranged with the author(s). Unless otherwise
specified, the author of the codes is Aurore Delaigle.
Please contact me if you find errors in the codes.

Matlab and R codes for computing deconvolution
kernel density estimator and errorsinvariable
regression estimator (data with measurement
errors, errorsinvariables) and datadriven
bandwidths. Corrects the numerous errors of the
R package "decon", especially for the
datadriven bandwidths.
WARNING: These are simplified Matlab codes which
do not use Fast Fourier Transform. The codes
used in my papers were written in C using FFT,
which is faster.
Matlab codes written by Aurore Delaigle: Right
click/save as to have correct formatting. Code
uses the function
outerop :
The
above Matlab codes have kindly been converted to R
codes by Tianying Wang:
 R code
for the paper Delaigle, A. and Meister, A.
(2011). Nonparametric Regression Analysis for
Group Testing Data. JASA, 106,
640650.

Achilleas Achilleos's codes for the
paper Achilleos, A. and Delaigle, A. (2012).
Local bandwidth selectors for deconvolution
kernel density estimation. Statistics and
Computing, 22,
563577

R code for the paper Delaigle, A. and
Hall, P. (2012). Effect of HeavyTails on Ultra
High Dimensional Variable Ranking Methods.
Statistica Sinica, 22, 909932.

R code and
functions for the paper Delaigle, A. and
Hall, P. (2012). Achieving nearperfect
classification for functional data. JRSS,B
, 74,
267286.
The codes were rewritten to make them more
readable, which might have introduced errors.
Please let me know if you find errors.
Publications
In Journals or books:
 Howitt, G., Melatos, A. and
Delaigle, A. (to appear). Nonparametric estimation of the size and waiting timedistributions of pulsar glitches. The
Astrophysical Journal.
 Datta, G., Delaigle, A., Hall,
P. and Wang, L. (2018). Semiparametric
prediction intervals in small areas when
auxiliary data are measured with error.
Statistica Sinica, 28,
pages TBA.
 Chang, J., Delaigle, A., Hall,
P. and Tang, CY. (2018). A frequency
domain analysis of the error distribution from
noisy highfrequency data. Biometrika,
105,
353369.
 Delaigle, A. and Hall, P.
(2016). Approximating fragmented functional data
by segments of Markov chains. Biometrika,
103,
779799.
 Delaigle, A. (2016). Peter
Hall's main contributions to deconvolution.
Annals of Statistics, 44,
18541866. pdf
file
 Delaigle, A., Hall, P. and
Zhou, W. (2016). Nonparametric
covariateadjusted regression. Annals of
Statistics, 44, 21902220. pdf
file
 Delaigle, A. and Wand, M.P.
(2016). A conversation with Peter Hall.
Statistical Science , 31, 275304.
pdf
file
 Delaigle, A., Meister, A. and
Rombouts, J. (2016). RootT consistent density
estimation in GARCH models. Journal of
Econometrics, 192, 5563.
 Delaigle, A. and Hall, P.
(2016). Methodology for nonparametric
deconvolution when the error distribution is
unknown. JRSS,B, 78, 231252.
pdf
file
Delaigle,
A. and Hall, P. (2015). Nonparametric methods
for group testing data, taking dilution into
account. Biometrika, 102,
871887. pdf
file
 Delaigle, A., Hall, P. and
Jamshidi, F. (2015). Confidence bands in
nonparametric errorsinvariables regression.
JRSS,B, 77,
149169. pdf
file and supplement.
DOI: 10.1111/rssb.12067
 Delaigle, A. and Zhou, W.
(2015). Nonparametric and parametric estimators
of prevalence from group testing data with
aggregated covariates. JASA, 110,
17851796.
 Delaigle, A. (2015).
Nonparametric kernel methods for curve
estimation and measurement errors.
Statistical Challenges in 21st Century
Cosmology, Proceedings of the International
Astronomical Union, IAU Symposium, 306,
2839,Cambridge University Press.
 Delaigle, A., Hall, P. and
Wishart, J. (2014). New approaches to non and
semiparametric regression for univariate and
multivariate group testing data. Biometrika,
101,
567585. pdf
file
 Delaigle, A. and Hall, P.
(2014). Parametrically assisted nonparametric
estimation of a density in the deconvolution
problem. JASA, 109,
717729. pdf
file
 Delaigle, A. (2014).
Nonparametric kernel methods with
errorsinvariables: constructing estimators,
computing them, and avoiding common mistakes.
Australian and New Zealand Journal of
Statistics, 56, 105124. (invited review
paper). pdf
file
 Buonaccorsi, J. and Delaigle,
A. (2014). Measurement Error. In The Work
of Raymond J Carroll  The Impact and
Influence of a Statistician. Edited by M.
Davidian, X. Lin, J. Morris, and L. Stefanski.
Springer.
 Delaigle, A. and Hall, P.
(2013). Classification using censored functional
data. JASA, 108, 12691283.
pdf file DOI: 10.1080/01621459.2013.824893
(this paper is about
classification of partially observed
functional data)
 Carroll, R.J., Delaigle, A.,
Hall, P. (2013). Unexpected properties of
bandwidth choice when smoothing discrete data
for constructing a functional data classifier.
Annals of Statistics, 41,
27392767.
DOI: 10.1214/13AOS1158. pdf
file
 Bennett, M., Melatos, A.,
Delaigle, A. and Hall, P. (2013). Reanalysis of
FStatistic GravitationalWave Searches with the
Higher criticism Statistic. The
Astrophysical Journal , 766, 99 (10
pages).
pdf file
 Delaigle, A. and Hall, P.
(2012). Comment: Robustness to Assumption of
Normally Distributed Errors. JASA, 107,
10361039 (DOI:10.1080/01621459.2012.711730)
 Carroll, R.J., Delaigle, A.,
Hall, P. (2012). Deconvolution When Classifying
Noisy Data Involving Transformations. JASA,
107,
11661177 (DOI:10.1080/01621459.2012.699793). pdf
file
(this paper is about
classification of functional data or spatial
data observed with noise)
 Delaigle, A. and Hall, P.
(2012). Methodology and theory for partial least
squares applied to functional data. Annals
of Statistics, 40, 322352.
doi 10.1214/11AOS958
pdf file
 Delaigle, A., Hall, P. and
Bathia, N. (2012). Componentwise classification
and clustering of functional data.
Biometrika, 99, 299313.
pdf file and supplement
 Delaigle, A. and Hall, P.
(2012). Nonparametric regression with
homogeneous group testing data. Annals of
Statistics, 40, 131158.
doi 10.1214/11AOS952
pdf file
 Delaigle, A. and Hall, P.
(2012). Achieving nearperfect classification
for functional data. JRSS,B, 74,
267286
doi 10.1111/j.14679868.2011.01003.x
pdf file
Note: for the wheat data,
"protein content" should be replaced by
"moisture level". In the simulated example 3,
{ should be replaced by {
 Delaigle, A. and Hall, P.
(2012). Effect of HeavyTails on Ultra High
Dimensional Variable Ranking Methods.
Statistica Sinica, 22, 909932.
pdf
file
 Achilleos, A. and Delaigle,
A.(2012). Local bandwidth selectors for
deconvolution kernel density estimation.
Statistics and Computing, 22,
563577
DOI: 10.1007/s112220119247y.
pdf file and
supplement
 Delaigle, A. and Meister, A.
(2011). Nonparametric Regression Analysis for
Group Testing Data. JASA, 106, 640650.
pdf file Note: look in Delaigle and Hall
(2012) for corrected graphs of the real data
analysis.
Note 2: in the appendix,there is a "+" missing
between epsilon_J and O(epsilon_J^2) at line 8
of page 6
 Carroll, R.J., Delaigle, A.,
Hall, P. (2011). Testing and estimating
shapeconstrained nonparametric density and
regression in the presence of measurement error.
JASA, 106,
191202
pdf file
 Delaigle, A., Hall, P. and Jin,
J. (2011). Robustness and accuracy of methods
for high dimensional data analysis based on
Student's t statistic. JRSS,B, 73,
283301,
DOI: 10.1111/j.14679868.2010.00761.x pdf
file
 Delaigle, A. and Meister, A.
(2011). Nonparametric function estimation under
Fourieroscillating noise. Statistica
Sinica, 21,
10651092.
(this paper is about
deconvolution by kernel methods when the
characteristic function of the measurement
errors has some zeros)
DOI:10.5705/ss.2009.082. pdf
file
 Delaigle, A. and Hall, P.
(2011). Estimation of observationerror variance
in errorsinvariables regression.
Statistica Sinica, 21,
10231063.
DOI:10.5705/ss.2009.039. pdf
file
 Delaigle, A. and Meister, A.
(2011). Rateoptimal nonparametric estimation in
classical and Berkson errorsinvariables
problems. Journal of Statistical Planning
and Inference, 141, 102114. pdf
file
 Delaigle, A. and Hall, P.
(2011). Theoretical properties of principal
component score density estimators in functional
data analysis. Vestnik of StPetersburg
university, Ser. 1 (Mathematics,
Mechanics, Astronomy), 2011, Issue 2, 5569.
 Delaigle, A. (2010). Discussion
of the paper Maximum Likelihood estimator
of a multidimensional logconcave density
by M. Cule, R. Samworth and M. Stewart.
JRSS, B, 72,
578579.
 Delaigle, A. and Hall, P.
(2010). Defining probability density for a
distribution of random functions. Annals of
Statistics, 38, 11711193. pdf
file
 Chen, S.X., Delaigle, A. and
Hall, P. (2010). Nonparametric Estimation for a
class of Levy process. Journal of
Econometrics, 157, 257271.
pdf file.
 Delaigle, A. and Hall, P.
(2010). Discussion of the paper "Identification
and Estimation of Nonlinear Models Using Two
Samples with Nonclassical Measurement Errors" by
Carroll, Chen and Hu. Journal of Nonparametric Statistics,
22,
401404.
 Delaigle, A. and Hall, P.
(2010). Kernel methods and minimum contrast
estimators for empirical deconvolution. In
Probability and Mathematical Genetics, Papers
in Honour of Sir John Kingman, London
Mathematical Society Lecture Note Series.
Chapter 8. Edited by N.H. Bingham and
C.M. Goldie. Cambridge University Press.
pdf file.
 Delaigle, A., Fan, J. and
Carroll, R.J. (2009). A Designadaptive Local
Polynomial Estimator for the ErrorsinVariables
Problem. JASA, 104, 348359
pdf file
Errata: the main theorem is missing some of the
conditions of Lemma 1. However these conditions
(on h) can be avoided if the asymptotic
normality result is not stated as a ratio.
 Carroll, R.J., Delaigle, A.,
Hall, P. (2009). Nonparametric Prediction in
Measurement Error Models. JASA, 104,
9931003.
pdf file
 Carroll, R.J., Delaigle, A.,
Hall, P. (2009). Nonparametric Prediction in
Measurement Error Models: Rejoinder. JASA,
104,
10131014.
 Delaigle, A., Hall, P. and
Apanasovich, T. (2009). Weighted least squares
methods for prediction in the functional data
linear model. Electronic Journal of
Statistics, 3, 865885
pdf file
 Delaigle, A. and Hall, P.
(2009). Higher criticism in the context of
unknown distribution, nonindependence and
classification. In Perspectives in
mathematical sciences I: Probability and
Statistics. Chapter 6 (page 109138),
Edited by N. Sastry, M. Delampady, B. Rajeev and
TSSRK Rao. World Scientific Publishing.
pdf file
 Delaigle, A. (2008). An
alternative view of the deconvolution problem. Statistica Sinica,
18,
10251045.
pdf file (this
paper explains why using the formula for
Laplace errors when the error is not Laplace,
often gives good practical results)
 Delaigle, A. and Hall, P.
(2008). Using SIMEX for smoothingparameter
choice in errorsinvariables problems.
JASA, 103,
280287
pdf file and the technical details written
in the format of an older version: Technical
details of older version
Errata: Figure 3 shows case (c), not case (d);
Error variance in case (d): 0.0028 should be
sqrt(0.0028).
 Delaigle, A., Hall, P. and
Meister, A. (2008). On Deconvolution with
repeated measurements. Annals of
Statistics, 36,
665685, doi:10.1214/009053607000000884.
published pdf file or an
older
version that contains more technical details
 Delaigle, A. and Meister, A.
(2008). Density
estimation
with heteroscedastic error.
Bernoulli, 14,
562579, doi:10.3150/08BEJ121 .
 Delaigle, A., Hall, P. and
Muller, HG. (2007). Accelerated convergence for
nonparametric regression with coarsened
predictors. Annals of Statistics, 35, 26392653,
doi:10.1214/009053607000000497.
pdf file
 Delaigle, A. and Meister, A.
(2007). Nonparametric regression estimation in
the heteroscedastic errorsinvariables problem.
JASA , 102,
14161426 ,
doi:10.1198/016214507000000987
pdf file
 Carroll, R.J., Delaigle, A.,
Hall, P. (2007). Nonparametric regression
estimation from data contaminated by a mixture
of Berkson and classical errors. JRSS,B,
69,
859878 , DOI:
10.1111/j.14679868.2007.00614.x . pdf
file
 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, DOI:
10.1007/s1122200790240, pdf
file
This paper explains how
to compute the deconvolution kernel estimator
in practice and all sorts of numerical issues
arising when not calculating the estimator
correctly.
 Muller, HG., Wang, JL., Yu,
W., Delaigle, A. and Carey, J. (2007). Survival
and Aging in the Wild via Residual Demography.
Theoretical Population Biology, 72, 513522
 Delaigle, A. (2007).
Nonparametric density estimation from data with
a mixture of Berkson and classical errors. Canadian Journal
of Statistics, 35, 89 104.
 Delaigle, A. and Hall, P.
(2006). On optimal kernel choice for
deconvolution. Statistics and
Probability Letters, 76,
1594–1602.
 Delaigle, A., Hall, P. and Qiu,
P. (2006). Nonparametric methods for solving the
Berkson errorsinvariables problem. Journal
of the Royal Statistical Society, B, 68, 201220.
 Delaigle, A. and I. Gijbels
(2006). Estimation of boundary and discontinuity
points in deconvolution problems. Statistica
Sinica,
16, 773 788. Long
version
 Delaigle, A. and I. Gijbels
(2006). Datadriven boundary estimation in
deconvolution problems. Computational
Statistics and Data Analysis, 50, 1965 
1994.
 Delaigle, A. and I. Gijbels
(2004). Bootstrap bandwidth selection in kernel
density estimation from a contaminated sample, Annals
of the Institute of Statistical Mathematics,
56, 19 
47.
 Delaigle, A. and I. Gijbels
(2004). Practical bandwidth selection in
deconvolution kernel density estimation, Computational
Statistics and Data Analysis, 45, 249 
267.
pdf file
 Delaigle, A. and I. Gijbels
(2002). Estimation of integrated squared density
derivatives from a contaminated sample, Journal
of the Royal Statistical Society, B, 64, 869886.
.
Other work:
 Soetewey
S., Delaigle A., Baguette M., Leboulengé E., Rolin
JM. (1999). Exploiting regional data sets for the
comparison of population structures: a case study
for three common butterflies. Manuscript.
Contact information
School of Mathematics
and Statistics,
University of Melbourne
Victoria
3010
Australia 
(here,
& stands for at)

