U Melbourne Faculty of Science
School of Mathematics and Statistics



Aurore Delaigle


Degrees

Awards

Previous positions
Editorial Service
Planned visits
Code
Publications
Funding for foreign students
Statistical ressources
LaTeX ressources
Other links




















Aurore Delaigle

Degrees

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 MathematicsUniversity of Bristol
Assistant professor at the Department of MathematicsUniversity of California at San Diego
BAEF postdoctoral fellow at the Department of Statistics, University of California at Davis


Fellowhips and awards

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.


Publications

      In Journals or books:
  • 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 covariate-adjusted regression. Annals of Statistics. pdf file
  • Delaigle, A. and Wand, M.P. (2016). A conversation with Peter Hall. Statistical Science , 31, 275-304
  • Delaigle, A., Meister, A. and Rombouts, J. (2016). Root-T consistent density estimation in GARCH models. Journal of Econometrics, 192, 55-63.
  • Delaigle, A. and Hall, P. (2016). Methodology for nonparametric deconvolution when the error distribution is unknown. JRSS,B, 78, 231-252. pdf file

  • Delaigle, A. and Hall, P. (2015). Nonparametric methods for group testing data, taking dilution into account. Biometrika, 102, 871-887. pdf file
  • Delaigle, A., Hall, P. and Jamshidi, F. (2015). Confidence bands in nonparametric errors-in-variables regression. JRSS,B, 77, 149-169. 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, 1785-1796.
  • 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, 28-39,Cambridge University Press.

  • Delaigle, A., Hall, P. and Wishart, J. (2014). New approaches to non- and semi-parametric regression for univariate and multivariate group testing data. Biometrika, 101, 567-585. pdf file
  • Delaigle, A. and Hall, P. (2014). Parametrically assisted nonparametric estimation of a density in the deconvolution problem. JASA, 109, 717-729. pdf file
  • Delaigle, A. (2014). Nonparametric kernel methods with errors-in-variables: constructing estimators, computing them, and avoiding common mistakes. Australian and New Zealand Journal of Statistics, 56, 105-124. (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, 1269-1283. 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, 2739-2767.
    DOI: 10.1214/13-AOS1158. pdf file

  • Bennett, M., Melatos, A., Delaigle, A. and Hall, P. (2013). Reanalysis of F-Statistic Gravitational-Wave 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, 1036-1039 (DOI:10.1080/01621459.2012.711730)
  • Carroll, R.J., Delaigle, A., Hall, P. (2012). Deconvolution When Classifying Noisy Data Involving Transformations. JASA, 107, 1166-1177 (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, 322-352.
    doi 10.1214/11-AOS958 pdf file
  • Delaigle, A., Hall, P. and Bathia, N. (2012). Componentwise classification and clustering of functional data. Biometrika, 99, 299-313.
    pdf file and supplement
  • Delaigle, A. and Hall, P. (2012). Nonparametric regression with homogeneous group testing data. Annals of Statistics, 40, 131-158.
    doi 10.1214/11-AOS952 pdf file
  • Delaigle, A. and Hall, P. (2012). Achieving near-perfect classification for functional data. JRSS,B, 74, 267--286
    doi 10.1111/j.1467-9868.2011.01003.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 Heavy-Tails on Ultra High Dimensional Variable Ranking Methods. Statistica Sinica, 22, 909-932. pdf file
  • Achilleos, A. and Delaigle, A.(2012). Local bandwidth selectors for deconvolution kernel density estimation. Statistics and Computing, 22, 563-577
    DOI: 10.1007/s11222-011-9247-y. pdf file and supplement


  • Delaigle, A. and Meister, A. (2011). Nonparametric Regression Analysis for Group Testing Data. JASA, 106, 640-650. 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 shape-constrained nonparametric density and regression in the presence of measurement error. JASA, 106, 191-202 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, 283-301,
    DOI: 10.1111/j.1467-9868.2010.00761.x pdf file
  • Delaigle, A. and Meister, A. (2011). Nonparametric function estimation under Fourier-oscillating noise. Statistica Sinica, 21, 1065-1092.
    (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 observation-error variance in errors-in-variables regression. Statistica Sinica, 21, 1023-1063.
    DOI:10.5705/ss.2009.039. pdf file
  • Delaigle, A. and Meister, A. (2011). Rate-optimal nonparametric estimation in classical and Berkson errors-in-variables problems. Journal of Statistical Planning and Inference, 141, 102-114. pdf file
  • Delaigle, A. and Hall, P. (2011). Theoretical properties of principal component score density estimators in functional data analysis. Vestnik of St-Petersburg university, Ser. 1 (Mathematics, Mechanics, Astronomy), 2011, Issue 2, 55-69.


  • Delaigle, A. (2010). Discussion of the paper Maximum Likelihood estimator of a multidimensional log-concave density by M. Cule, R. Samworth and M. Stewart. JRSS, B, 72, 578-579.
  • Delaigle, A. and Hall, P. (2010). Defining probability density for a distribution of random functions. Annals of Statistics, 38, 1171-1193. pdf file
  • Chen, S.X., Delaigle, A. and Hall, P. (2010). Nonparametric Estimation for a class of Levy process. Journal of Econometrics, 157, 257-271. 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, 401-404.
  • 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 Design-adaptive Local Polynomial Estimator for the Errors-in-Variables Problem. JASA, 104, 348-359 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, 993-1003. pdf file
  • Carroll, R.J., Delaigle, A., Hall, P. (2009). Nonparametric Prediction in Measurement Error Models: Rejoinder. JASA, 104, 1013-1014.
  • 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, 865-885 pdf file
  • Delaigle, A. and Hall, P. (2009). Higher criticism in the context of unknown distribution, non-independence and classification. In Perspectives in mathematical sciences I: Probability and Statistics. Chapter 6 (page 109-138), 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, 1025-1045. 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 smoothing-parameter choice in errors-in-variables problems. JASA, 103, 280-287 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, 665-685, 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, 562-579, doi:10.3150/08-BEJ121 .


  • Delaigle, A., Hall, P. and Muller, H-G. (2007). Accelerated convergence for nonparametric regression with coarsened predictors. Annals of Statistics, 35, 2639-2653, doi:10.1214/009053607000000497. pdf file
  • Delaigle, A. and Meister, A. (2007). Nonparametric regression estimation in the heteroscedastic errors-in-variables problem. JASA , 102, 1416-1426 , 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, 859-878 , DOI: 10.1111/j.1467-9868.2007.00614.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/s11222-007-9024-0, 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, H-G., Wang, J-L., Yu, W., Delaigle, A. and Carey, J. (2007). Survival and Aging in the Wild via Residual Demography. Theoretical Population Biology, 72, 513-522
  • 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 errors-in-variables problem. Journal of the Royal Statistical Society, B, 68, 201-220.
  • 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). Data-driven 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, 869-886. .
      Other work:
  • Soetewey S., Delaigle A., Baguette M., Leboulengé E., Rolin J-M. (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

Phone:  +61 (0) 3 8344 9731
Fax:  +61 (0)3 8344 4599
E-mail:  A.Delaigle & ms.unimelb.edu.au
(here, & stands for at)