Editorial service

Past: Co-Editor-in-chief of JRSS,B (Jan 2020 to Dec 2022, handling revisions until June 2023),
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 errors-in-variable regression estimator (data with measurement errors, errors-in-variables) and data-driven bandwidths. Corrects the numerous errors of the R package "decon", especially for the data-driven 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 :


• Code for computing the deconvolution kernel density estimator
• Code for computing the plug-in bandwidth of Delaigle and Gijbels (2002, 2004)
• Code for computing the CV bandwidth of Stefanski and Carroll (1990)
• An example illustrating how to use the various functions
• Code for computing the local constant deconvolution estimator of Fan and Truong (1993)
• Code for computing the SIMEX bandwidth of Delaigle and Hall (2008). Requires this routine and this routine .
• Code for computing the deconvolution kernel density estimator in the heteroscedastic error case as in Delaigle and Meister (2008).
• Code for computing the plug-in bandwidth in the heteroscedastic error case as in Delaigle and Meister (2008)
• An example illustrating how to use the various functions in the heteroscedastic error case
• Code for generating from a Laplace distribution

The above Matlab codes have kindly been converted to R codes by Tianying Wang:


• Download R package for deconvolution kernel estimator

We have a deconvolve R package (with Tim Hyndman and Tianying Wang) that is in the last debugging phase:

• Deconvolve R package
  




• R code for the paper Delaigle, A. and Meister, A. (2011). Nonparametric Regression Analysis for Group Testing Data. JASA, 106, 640-650.
• 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, 563-577
• R code for the paper Delaigle, A. and Hall, P. (2012). Effect of Heavy-Tails on Ultra High Dimensional Variable Ranking Methods. Statistica Sinica, 22, 909-932.
• R code and functions for the paper Delaigle, A. and Hall, P. (2012). Achieving near-perfect classification for functional data. JRSS,B , 74, 267-286.
  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:

• Camirand-Lemyre, F., Carroll, R.J. and Delaigle, A. (to appear). Nonparametric density estimation of a long-term trend from repeated semi-continuous data. JASA.
• Delaigle, A., Meister, A. and Zhang, J. (to appear). Nonparametric curve estimation in measurement error problems with conditionally heteroscedastic variances. Bernoulli.
• Delaigle, A., Tan, R. (2024). Group testing regression analysis with missing data and imperfect tests. Statistica Sinica, 34, 201-228. pdf file
• Delaigle, A., Tan, R. (2023). Group testing regression analysis with covariates and specimens subject to missingness. Statistics in Medicine, 42, 731-744. pdf file
• Camirand-Lemyre, F., Carroll, R.J. and Delaigle, A. (2022). Semiparametric Estimation of the Distribution of Episodically Consumed Foods Measured with Error. JASA, 117, 469-481. pdf file and Matlab code
• Delaigle, A., Hall, P. Huang, W. and Kneip, A. (2021). Estimating the covariance of fragmented and other related types of functional data. JASA, 116, 1383-1401. pdf file, appendix and Wei Huang's Matlab code
• Delaigle, A. and Meister, A. (2021). Nonparametric density estimation for intentionally corrupted functional data. Statistica Sinica. 31, 1-20. pdf file
  
• Delaigle, A. (2021). 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 (2021). 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. (2021). 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, 467-480. pdf file, Code
• Delaigle, A., Hall, P. and Pham, T. (2019). Clustering functional data into groups using projections. JRSS,B, 81, 271-304. 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). Semi-parametric prediction intervals in small areas when auxiliary data are measured with error. Statistica Sinica, 28, 2309-2335. pdf file
  
• Chang, J., Delaigle, A., Hall, P. and Tang, C-Y. (2018). A frequency domain analysis of the error distribution from noisy high-frequency data. Biometrika, 105, 353-369. pdf file Matlab Code
  

• Delaigle, A. and Hall, P. (2016). Approximating fragmented functional data by segments of Markov chains. Biometrika, 103, 779-799. pdf file
  
• Delaigle, A. (2016). Peter Hall's main contributions to deconvolution. Annals of Statistics, 44, 1854-1866. pdf file
  
• Delaigle, A., Hall, P. and Zhou, W. (2016). Nonparametric covariate-adjusted regression. Annals of Statistics, 44, 2190-2220. pdf file
  
• Delaigle, A. and Wand, M.P. (2016). A conversation with Peter Hall. Statistical Science , 31, 275-304. pdf file
  
• Delaigle, A., Meister, A. and Rombouts, J. (2016). Root-T consistent density estimation in GARCH models. Journal of Econometrics, 192, 55-63. pdf file
  
• Delaigle, A. and Hall, P. (2016). Methodology for nonparametric deconvolution when the error distribution is unknown. JRSS,B, 78, 231-252. 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, 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. 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, 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.
  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 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. .