Soumendra Lahiri

Zhou, W., & Lahiri, S. (2023, August 25). Stationary Jackknife. JOURNAL OF TIME SERIES ANALYSIS. https://doi.org/10.1111/jtsa.12714

Bennett, I., Martin, D. E. K., & Lahiri, S. N. (2022, December 15). Fitting sparse Markov models through a collapsed Gibbs sampler. COMPUTATIONAL STATISTICS. https://doi.org/10.1007/s00180-022-01310-8

Chakraborty, A., Lahiri, S. N., & Wilson, A. (2020). A STATISTICAL ANALYSIS OF NOISY CROWDSOURCED WEATHER DATA. ANNALS OF APPLIED STATISTICS, 14(1), 116–142. https://doi.org/10.1214/19-AOAS1290

Van Hala, M., Bandyopadhyay, S., Lahiri, S. N., & Nordman, D. J. (2020). A general frequency domain method for assessing spatial covariance structures. BERNOULLI, 26(4), 2463–2487. https://doi.org/10.3150/19-BEJ1160

Lee, D. H., Lahiri, S. N., & Sinha, S. (2020). A test of homogeneity of distributions when observations are subject to measurement errors. BIOMETRICS, 76(3), 821–833. https://doi.org/10.1111/biom.13207

Giordano, F., Lahiri, S. N., & Parrella, M. L. (2020). GRID: A VARIABLE SELECTION AND STRUCTURE DISCOVERY METHOD FOR HIGH DIMENSIONAL NONPARAMETRIC REGRESSION. ANNALS OF STATISTICS, 48(3), 1848–1874. https://doi.org/10.1214/19-AOS1846

Bugni, F. A., Caner, M., Kock, A. B., & Lahiri, S. (2020). Inference in partially identified models with many moment inequalities using Lasso. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 206, 211–248. https://doi.org/10.1016/j.jspi.2019.09.013

Das, D., & Lahiri, S. N. (2019). Distributional consistency of the lasso by perturbation bootstrap. BIOMETRIKA, 106(4), 957–964. https://doi.org/10.1093/biomet/asz029

Lahiri, S. N., Das, U., & Nordman, D. J. (2019). Empirical Likelihood for a Long Range Dependent Process Subordinated to a Gaussian Process. JOURNAL OF TIME SERIES ANALYSIS, 40(4), 447–466. https://doi.org/10.1111/jtsa.12465

Das, D., Gregory, K., & Lahiri, S. N. (2019). PERTURBATION BOOTSTRAP IN ADAPTIVE LASSO. ANNALS OF STATISTICS, 47(4), 2080–2116. https://doi.org/10.1214/18-AOS1741

Shockley, K. R., Gupta, S., Harris, S. F., Lahiri, S. N., & Peddada, S. D. (2019). Quality Control of Quantitative High Throughput Screening Data. FRONTIERS IN GENETICS, 10. https://doi.org/10.3389/fgene.2019.00387

Das, D., & Lahiri, S. N. (2019). Second order correctness of perturbation bootstrap M-estimator of multiple linear regression parameter. BERNOULLI, 25(1), 654–682. https://doi.org/10.3150/17-BEJ1001

Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A SMOOTH BLOCK BOOTSTRAP FOR QUANTILE REGRESSION WITH TIME SERIES. ANNALS OF STATISTICS, 46(3), 1138–1166. https://doi.org/10.1214/17-aos1580

Chatterjee, A., & Lahiri, S. N. (2018). EDGEWORTH EXPANSIONS FOR A CLASS OF SPECTRAL DENSITY ESTIMATORS AND THEIR APPLICATIONS TO INTERVAL ESTIMATION. STATISTICA SINICA, 28(4), 2591–2608. https://doi.org/10.5705/ss.202017.0121

Lahiri, S. N., Politis, D. N., & Robinson, P. M. (2018). Emanuel Parzen memorial. Journal of Time Series Analysis, 39(3), 241–241.

Kim, Y. M., Lahiri, S. N., & Nordman, D. J. (2018). Non-Parametric Spectral Density Estimation Under Long-Range Dependence. JOURNAL OF TIME SERIES ANALYSIS, 39(3), 380–401. https://doi.org/10.1111/jtsa.12284

Lahiri, S. N. (2018). Uncertainty Quantification in Robust Inference for Irregularly Spaced Spatial Data Using Block Bootstrap. SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY, 80, 173–221. https://doi.org/10.1007/s13171-018-0154-6

Van Hala, M., Bandyopadhyay, S., Lahiri, S. N., & Nordman, D. J. (2017). On the non-standard distribution of empirical likelihood estimators with spatial data. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 187, 109–114. https://doi.org/10.1016/j.jspi.2017.02.007

Cleghern, Z., Lahiri, S., Ozaltin, O., & Roberts, D. L. (2017). Predicting future states in dota 2 using value-split models of time series attribute data. Proceedings of the 12th International Conference on the Foundations of Digital Games (FDG'17).

Nordman, D. J., & Lahiri, S. N. (2016, October). A discussion of Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions by L. Pan and DN Politis. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, Vol. 177, pp. 35–40. https://doi.org/10.1016/j.jspi.2015.10.014

Lahiri, S. N., & Robinson, P. M. (2016). Central limit theorems for long range dependent spatial linear processes. BERNOULLI, 22(1), 345–375. https://doi.org/10.3150/14-bej661

Bandypadhyay, S., Lahiri, S. N., & Nordman, D. J. (2015). A FREQUENCY DOMAIN EMPIRICAL LIKELIHOOD METHOD FOR IRREGULARLY SPACED SPATIAL DATA. ANNALS OF STATISTICS, 43(2), 519–545. https://doi.org/10.1214/14-aos1291

Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2015). A SMOOTH BLOCK BOOTSTRAP FOR STATISTICAL FUNCTIONALS AND TIME SERIES. JOURNAL OF TIME SERIES ANALYSIS, 36(3), 442–461. https://doi.org/10.1111/jtsa.12117

Gregory, K. B., Carroll, R. J., Baladandayuthapani, V., & Lahiri, S. N. (2015). A Two-Sample Test for Equality of Means in High Dimension. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 110(510), 837–849. https://doi.org/10.1080/01621459.2014.934826

Chatterjee, A., & Lahiri, S. N. (2015). Comment. Journal of the American Statistical Association, 110(512), 1434–1438. https://doi.org/10.1080/01621459.2015.1102143

Chatterjee, A., Gupta, S., & Lahiri, S. N. (2015). On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property. JOURNAL OF ECONOMETRICS, 186(2), 317–324. https://doi.org/10.1016/j.jeconom.2015.02.012

Staicu, A.-M., Lahiri, S. N., & Carroll, R. J. (2015). Significance tests for functional data with complex dependence structure. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 156, 1–13. https://doi.org/10.1016/j.jspi.2014.08.006

Nordman, D. J., & Lahiri, S. N. (2014). [Review of A review of empirical likelihood methods for time series]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 155, 1–18. https://doi.org/10.1016/j.jspi.2013.10.001

Gupta, S., & Lahiri, S. N. (2014, July 3). Comment. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 109, pp. 1013–1015. https://doi.org/10.1080/01621459.2014.905789

Contemporary developments in statistical theory: A festschrift for Hira Lal Koul. (2014). Cham: Springer.

Lahiri, S., & Xie, M.-ge. (2014). Preface to the Professor Kesar Singh Memorial Issue. Statistical Methodology, 20, 1. https://doi.org/10.1016/J.STAMET.2014.04.002

Lahiri, S., Schick, A., SenGupta, A., & Sriram, T. N. (2014). Professor Hira Lal Koul’s Contribution to Statistics. In S. Lahiri, A. Schick, A. SenGupta, & T. Sriram (Eds.), Contemporary Developments in Statistical Theory (pp. 1–7). https://doi.org/10.1007/978-3-319-02651-0_1

DasGupta, A., Lahiri, S. N., & Stoyanov, J. (2014). Sharp fixed n bounds and asymptotic expansions for the mean and the median of a Gaussian sample maximum, and applications to the Donoho-Jin model. STATISTICAL METHODOLOGY, 20, 40–62. https://doi.org/10.1016/j.stamet.2014.01.002

Akritas, M. G., Lahiri, S. N., Dimitris N., & Politis, D. N. (Eds.). (2014). Topics in nonparametric statistics: Proceedings of the first Conference of the International Society for Nonparametric Statistics. Berlin: Springer.

Nordman, D. J., Bunzel, H., & Lahiri, S. N. (2013). A NONSTANDARD EMPIRICAL LIKELIHOOD FOR TIME SERIES. ANNALS OF STATISTICS, 41(6), 3050–3073. https://doi.org/10.1214/13-aos1174

Kim, Y. M., Lahiri, S. N., & Nordman, D. J. (2013). A Progressive Block Empirical Likelihood Method for Time Series. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 108(504), 1506–1516. https://doi.org/10.1080/01621459.2013.847374

Chatterjee, A., & Lahiri, S. N. (2013). Rates of convergence of the adaptive LASSO estimators to the Oracle distribution and higher order refinements by the bootstrap. Annals of Statistics, 41(3), 1232–1259. https://doi.org/10.1214/13-aos1106

Nordman, D. J., & Lahiri, S. N. (2012). Block Bootstraps for Time Series With Fixed Regressors. Journal of the American Statistical Association, 107(497), 233–246. https://doi.org/10.1080/01621459.2011.646929

Maitra, R., Melnykov, V., & Lahiri, S. N. (2012). Bootstrapping for Significance of Compact Clusters in Multidimensional Datasets. Journal of the American Statistical Association, 107(497), 378–392. https://doi.org/10.1080/01621459.2011.646935

Mukhopadhyay, S., Parzen, E., & Lahiri, S. N. (2012). From data to constraints. AIP Conference Proceedings, 1443(1), 32–39. https://doi.org/10.1063/1.3703617

Nordman, D. J., & Lahiri, S. N. (2011). Bias expansion of spatial statistics and approximation of differenced lattice point counts. Proceedings - Mathematical Sciences, 121(2), 229–244. https://doi.org/10.1007/s12044-011-0024-9

Lahiri, S. N., & Mukhopadhyay, S. (2011). Comments on: Subsampling weakly dependent time series and application to extremes. TEST, 20(3), 491–496. https://doi.org/10.1007/S11749-011-0273-Z

Chatterjee, A., & Lahiri, S. N. (2011). Strong consistency of Lasso estimators. Sankhya A, 73(1), 55–78. https://doi.org/10.1007/S13171-011-0006-0

Bandyopadhyay, S., & Lahiri, S. N. (2010). Resampling-based bias-corrected time series prediction. Journal of Statistical Planning and Inference, 140(12), 3775–3788. https://doi.org/10.1016/j.jspi.2010.04.042

Lahiri, S. N., Furukawa, K., & Lee, Y.-D. (2007). A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods. Statistical Methodology, 4(3), 292–321. https://doi.org/10.1016/j.stamet.2006.08.002

Zhu, J., & Lahiri, S. N. (2007). Bootstrapping the Empirical Distribution Function of a Spatial Process. Statistical Inference for Stochastic Processes, 10(2), 107–145. https://doi.org/10.1007/s11203-005-2349-4

Lahiri, S. N., Chatterjee, A., & Maiti, T. (2007). Normal approximation to the hypergeometric distribution in nonstandard cases and a sub-Gaussian Berry–Esseen theorem. Journal of Statistical Planning and Inference, 137(11), 3570–3590. https://doi.org/10.1016/j.jspi.2007.03.033

Nordman, D. J., & Lahiri, S. N. (2005). Validity of the Sampling Window Method for Long-range dependent linear processes. Econometric Theory, 21(06). https://doi.org/10.1017/S0266466605050541

Lahiri, S. N., & Mukherjee, K. (2004). Asymptotic distributions of M-estimators in a spatial regression model under some fixed and stochastic spatial sampling designs. Annals of the Institute of Statistical Mathematics, 56(2), 225–250. https://doi.org/10.1007/bf02530543

Karabulut, I., & Lahiri, S. N. (1997). Two-term Edgeworth expansion for M-estimators of a linear regression parameter without Cramér-type conditions and an application to the bootstrap. Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics, 62(3), 361–370. https://doi.org/10.1017/S1446788700001063