I have a broad research interest in methodological, computational and theoretical research in Statistics motivated by substantial applications and interdisciplinary collaborations. Research directions include high dimensional data analysis, model selection, Bayesian computation, large-scale computational models, functional data, and quantile modeling.

Publications in Theory and Methodology

(Note: * indicates a graduate student author)

Wu, Teng.*, Narisetty, N.N., and Yang, Y. (2023). Statistical Inference via Conditional Bayesian Posteriors in High-Dimensional Linear Regression. Electronic Journal of Statistics.

Narisetty, N.N. and Koenker, R. (2022). Censored Quantile Regression Survival Models with a Cure Proportion. Journal of Econometrics 226 (1): 192 -203.

Gan, L.*, Narisetty, N.N., and Liang, F. (2022). Bayesian Modeling for Conditional Random Fields.  Statistica Sinica 32, 131-152.

Mrkvička, T., Myllymäki, M., Kuronen, M., Narisetty, N. N. (2022). New methods for multiple testing in permutation inference for the general linear model. Statistics in Medicine 41 (2), 276-297.

Benedetti, M., and Berrocal, V. J., and Narisetty, N.N. (2022). Identifying Regions of Inhomogeneities in Spatial Processes via an M-RA and Mixture Priors. Biometrics 78(2):798-811. (Honorable Mention at ENVR student paper competition for Marco Benedetti)

Narisetty, N.N. (2021). Theoretical and Computational Aspects of Continuous Spike-and-Slab Priors. Book chapter in Handbook of Bayesian Variable Selection published by Champman & Hall/CRC, 57-80.

* Li, K., Yang, Y., and Narisetty, N.N. (2021). Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit. Electronic Journal of Statistics 15(2): 5652-5695.

Wu, T.*, and Narisetty, N.N. (2021). Bayesian Multiple Quantile Regression with a Score Based Likelihood. Bayesian Analysis 16 (3): 875 – 903.

Yang, X.*, Gan, L.*, Narisetty, N.N., and Liang, F. (2021). GemBag: Group Estimation of Multiple Bayesian Graphical Models. Journal of Machine Learning Research 22(54):1−48.

Harris, T., Li, B., Steiger, N., Smerdon, J., Narisetty, N. N., Tucker, J.D. (2021). Evaluating Proxy Influence in Assimilated Paleoclimate Reconstructions—Testing the Exchangeability of Two Ensembles of Spatial Processes. Journal of the American Statistical Association 116: 1100-1113.

Jonathan Boss, J., Rix, A., Chen, Y., Narisetty, N.N., Wu, Z., Ferguson, K. K., McElrath, T. F., Meeker, J. D., and Mukherjee, B. (2021). A Hierarchical Integrative Group LASSO framework for analyzing environmental mixtures. Environmetrics 32 (8), e2698.

Yang, X.* and Narisetty, N.N. (2020). Group Selection with Hierarchical Bayesian Modeling. Bayesian Analysis. 15(3): 909-935.

Narisetty, N.N. (2020) Bayesian Model Selection for High Dimensional Data. Handbook of Statistics Volume 43, Principles and Methods for Data Science, Chapter 6, Elsevier.

Narisetty, N.N., Shen, J., and He, X. (2019). Skinny Gibbs: A Consistent and Scalable Gibbs Sampler for Model Selection. Journal of the American Statistical Association (Theory & Methods).

Gan, L.*, Narisetty, N.N., and Liang, F. (2019). Bayesian Regularization for Graphical Models with Unequal Shrinkage. Journal of the American Statistical Association (Theory & Methods).  (Winner of SBSS Student Paper Award by Lingrui Gan)

Gan, L.*, Yang, X.*, Narisetty, N.N., and Liang, F. (2019). Bayesian Joint Estimation of Multiple Graphical Models. Conference on Neural Information Processing Systems (Acceptance rate: 21%).

Narisetty, N.N., Bhramar Mukherjee, B., Chen, Y., Gonzalez, R., and Meeker, J.D. (2019). Selection of nonlinear interactions by a forward stepwise algorithm: Application to characterizing the health effects of environmental chemical mixtures. Statistics in Medicine.

Yang, X., Narisetty, N.N., He, X. (2018). A New Approach to Censored Quantile Regression Estimation. Journal of Computational and Graphical Statistics 27 (2) 417-425. Read Here

Lim, H., Narisetty, N.N., Cheon, S. (2017). Robust Multivariate Mixture Regression Models with Missing Information. Journal of Statistical Computation and Simulation 87 (2): 328 -347; Read Here

Narisetty, N.N., Nair, V.N. (2016). Extremal Notion of Depth and Central Regions for Functional Data. To appear in Journal of the American Statistical Association (Theory & Methods) (Winner of Nonparametric Statistics Student Paper Award); Download

Narisetty, N.N., He, X. (2015). Discussion of “Multivariate Functional Outlier Detection”.  Statistical Methods and Applications 24 (2) 209-216; Download

Narisetty, N.N., He, X. (2014). Bayesian Variable Selection with Shrinking and Diffusing Priors. The Annals of Statistics 42 (2), 789-817
(Winner of Statistical Learning and Data Mining Student Paper Award); Download

Publications in Application Areas

He, F., Posselt, D. J., Narisetty, N.N., Zarzycki, C. M. and Nair, V.N. (2018). Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones.  Monthly Weather Reviews.

Eckner, J. T., Rettman, A., Narisetty, N.N., Greer, J., Moore, B., Brimacombe, S., He, X., Broglio, S. P. (2016). Stability of an ERP-Based Measure of Brain Network Activation in Athletes: A New Electrophysiological Assessment Tool for Concussion. Brain Injury 30(9): 1075 – 1081; Read Here

Broglio, S.P., Pettman, A., Greer, J., Brimacombe, S., Moore, B., Narisetty, N.N., He, X., Eckner, J.T. (2016). Investigating a Novel Measure of Brain Networking following Sport Concussion. International Journal of Sports Medicine 37(9): 714 – 722; Read Here

Tait, A.R., Lewis, T.V., Nair, V.N., Narisetty, N.N., Malviya, S., Fagerlin, A. (2013). Informing the Uninformed: Optimizing the Consent Message Using a Fractional Factorial Design. JAMA Pediatrics1-7; Read Here

An, L.C., Demers, M., Kirch, M.A., Considine-Dunn S., Nair, V.N., Dasgupta, K., Narisetty, N.N., Resnicow, K.,  Ahluwalia,J. (2013). A Randomized Trial of an Avatar-Hosted Multiple Behavior Change Intervention for Young Adult Smokers. JNCI Monographs 209-21; Read Here