
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:
 Chang, J.,
Delaigle, A., Hall, P. and Tang, CY. (to appear).
A frequency domain analysis of the error
distribution from noisy highfrequency data.
Biometrika.
 Datta, G.,
Delaigle, A., Hall, P. and Wang, L. (to appear).
Semiparametric prediction intervals in small
areas when auxiliary data are measured with error.
Statistica Sinica.
 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)

