



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
CoEditorinchief
of JRSS,B.
Past: Associate Editor for the Annals of
Statistics, JASA, JCGS, JRSS,B, Australian
and New Zealand Journal of Statistics,
Statistica Sinica, 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
or R Codes for my most recent papers can
often be found on the journals'
webpages. Otherwise email me for a copy.
 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:
We
have a deconvolve R package (with Tim
Hyndman and Tianying Wang) that is in
the last debugging phase:

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:

CamirandLemyre, F., Carroll, R.J. and
Delaigle, A. (to appear). Semiparametric
Estimation of the Distribution of
Episodically Consumed Foods Measured
with Error. JASA.
pdf file and
Matlab
code
 Delaigle,
A., Hall, P. Huang, W. and Kneip, A. (to
appear). Estimating the covariance of
fragmented and other related types of
functional data. JASA. pdf
file, appendix and Wei Huang's Matlab
code
 Delaigle,
A. and Meister, A. (to appear).
Nonparametric density estimation for
intentionally corrupted functional data.
Statistica Sinica.
pdf
file

Delaigle, A. (to appear). Deconvolution kernel density estimator. In Handbook of Measurement Error Models. Edited by G. Yi , A. Delaigle, P. Gustafson, CRC press. pdf file

Delaigle, A. and I. Van Keilegom (to appear). Deconvolution with unknown error distribution. In Handbook of Measurement Error Models. Edited by G. Yi , A. Delaigle, P. Gustafson, CRC press. pdf file

G. Yi , Delaigle, A. and Gustafson, P. (to appear). Handbook on Measurement Error Models. CRC press.
 Delaigle,
A., Huang, W. and Lei, S. (2020).
Estimation of conditional prevalence
from group testing data with missing
covariates. JASA, 115,
467480.
pdf
file,
Code
 Delaigle,
A., Hall, P. and Pham, T. (2019).
Clustering functional data into groups
using projections. JRSS,B, 81,
271304. pdf
file Matlab code

Howitt, G., Melatos, A. and Delaigle, A.
(2018). Nonparametric estimation of the
size and waiting time distributions of
pulsar glitches. The Astrophysical
Journal, 867,
60. pdf
file
 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,
23092335. pdf file
 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. pdf file Matlab Code
 Delaigle,
A. and Hall, P. (2016). Approximating
fragmented functional data by segments
of Markov chains. Biometrika, 103,
779799. pdf
file

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. pdf file
 Delaigle,
A. and Hall, P. (2016). Methodology for
nonparametric deconvolution when the
error distribution is unknown.
JRSS,B, 78, 231252. pdf
file
Code in the
deconvolve R package
 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. pdf
file
 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,
1594â€“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., LeboulengĂ©
E., Rolin JM. (1999). Exploiting regional
data sets for the comparison of population
structures: a case study for three common
butterflies. Manuscript.
Contact information
aurored & unimelb.edu.au
(replace
& by @) 
+61 (0)3
8344 9731 
School of Mathematics and Statistics,
University of
Melbourne
Victoria 3010, Australia




