
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
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.
Publications
In Journals or
books:
 Delaigle, A.
and Hall, P. (to appear).
Approximating fragmented functional data by segments of Markov chains.
Biometrika.

Delaigle, A. (to appear).
Peter Hall's main contributions to deconvolution. Annals of
Statistics. pdf file

Delaigle, A., Hall, P. and Zhou, W. (to appear).
Nonparametric covariateadjusted regression. Annals of
Statistics. pdf file

Delaigle, A. and Wand, M.P. (2016).
A conversation with Peter Hall. Statistical Science , 31, 275304
 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
 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.
Funding
available
for foreign students
 Funding is available, but the competition is
tough.
See here for various sources of funding, including
grants to pay your
international fees, etc: Study
in
Australia
 Si vous faites déjà une thèse
en France, il y a aussi une autre source: cotutelle
grants
Contact information
School of Mathematics and Statistics,
University
of Melbourne
Victoria
3010
Australia 
(here,
&
stands for at)

