Arindam Chatterjee

Statistical Sciences Unit, Room 120
Indian Statistical Institute
7, S.J.S. Sansanwal Marg
New Delhi 110016
INDIA

E-mail: cha-at-isid-dot-ac-dot-in
Phone: +91-11-41493920
CV.

Preprints and Publications

  • Low rank matrix estimation and asymptotic theory of bootstrap . (with M. Choudhury and D. Das)

  • Statistical inference for subgraph counts and clustering coefficient using network sampling in a sparse Stochastic Block Model framework. (with A. Mandal)

  • Bootstrapping LASSO estimators under variable selection consistency in high dimensions and some higher order refinements. Bernoulli (2026), 32(4), 2865-2893, (with D. Das and S. N. Lahiri). Supplement.

  • Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression. Journal of the American Statistical Association (2025), 120(551), 1645-1656, (with D. Das and S. N. Lahiri). Supplementary Materials.

  • Inference problems in binary regression model with misclassified response. Journal of Statistical Planning and Inference (2024), 231,106121, (with T. Bandyopadhyay and A. Bhattacharya).

  • Regression models for group testing: Identifiability and asymptotics. Journal of Statistical Planning and Inference (2020), 204, 141-152, (with T. Bandyopadhyay).

  • Edgeworth expansions for a class of spectral density estimators and their applications to interval estimation. Statistica Sinica (2018), 28, 2591-2608, (with S. N. Lahiri). Supplement.

  • Discussion of the paper "An adaptive resampling test for detecting the presence of significant predictors." by I. W. McKeague and M. Qian.. Journal of the American Statistical Association (2015), 110 (512), 1434-1438, (with S. N. Lahiri).

  • On the residual empirical process based on the Adaptive Lasso in high dimensions and its functional oracle property.. Journal of Econometrics (2015), 186 (2), 317-324, (with S. Gupta and S. N. Lahiri).

  • Rates of convergence of the Adaptive Lasso estimators to the Oracle distribution and higher order refinements by the bootstrap.. Annals of Statistics (2013), 41 (3), 1232-1259, (with S. N. Lahiri). Supplement.

  • High dimensional classification when useful information comes from many, perhaps all features.. Journal of the Indian Statistical Association (2012), 50, 51-82, (with P. Hall).

  • Bootstrapping Lasso estimators. Journal of the American Statistical Association (2011), 106 (494), 608-625, (with S. N. Lahiri).

  • Strong consistency of Lasso estimators. Sankhya Ser. A (2011), 73 (1), 55-78, (with S. N. Lahiri).

  • Asymptotic properties of sample quantiles from a finite population. Annals of the Institute of Statistical Mathematics (2011), 63 (1), 157-179.

  • Asymptotic properties of the residual bootstrap for Lasso estimators. Proceedings of the American Mathematical Society (2011), 138 (12), 4479-4509, (with S. N. Lahiri).

  • A Berry-Esseen theorem for hypergeometric probabilities under minimal conditions.. Proceedings of the American Mathematical Society (2007), 135 (5), 1535-1545, (with S. N. Lahiri).

  • Normal approximation to the hypergeometric distribution in nonstandard cases and a sub-Gaussian Berry-Esseen theorem.. Journal of Statistical Planning and Inference (2007), 137 (11), 3570-3590, (with S. N. Lahiri and T. Maiti).

    Links

    Google
    ISID Library