High-dimensional and complex data

Vidyashankar, A.N. and Li, L. (2019): Ancestral inference for branching processes in random environments and an application to polymerase chain reaction, Stochastic Models, 35, 318-337.
Diao,G., Hanlon, B. and Vidyashankar, A.N. (2014): Multiple testing for high-dimensional data, Perspectives on Big Data Analysis: Methodologies and Applications, Contemporary Mathematics, 622, 95-107.
Diao, G. and Vidyashankar, A.N. (2013): Assessing genome-wide statistical significance in large p small n problems, Genetics, 781-783.
Hanlon, B. and Vidyashankar, A.N. (2011): Inference for quantitation parameters in polymerase chain reactions via branching processes with random effects, Journal of the American Statistical Association, 106, pp. 525-533.
Kuelbs, J. and Vidyashankar, A.N. (2010): Asymptotic inference for high-dimensional data, pre-print (contains many technical issues not covered in the paper).
Kuelbs, J. and Vidyashankar, A.N. (2010): Asymptotic inference for high-dimensional data, The Annals of Statistics, 38, 533-569.

Machine learning, Post-selection inference, & Stochastic optimization

Francisci,G., Agostinelli, C., Nieto-Reyes, A. and Vidyashankar, A.N. (2023): Analytical and statistical properties of local depth functions motivated by clustering applications, Electronic Journal of Statistics, 17(1), 688-722.
Park, W.B., Vidyashankar, A.N. and McElroy, T. (2020): Supervised clustering via implicit network, ArXiv.
Khalili, A. and Vidyashankar, A.N. (2019): Hypothesis testing in finite mixture of regressions: sparsity and model selection uncertainty, Canadian Journal of Statistics, 46, 429-457.
Vidyashankar, A.N. and Xu, J. (2015): Stochastic optimization using Hellinger distance, Proceedings of the winter simulation conference, pp.3702-3713.

Gonz´alez, M., Minuesa, C. del Puerto, I., Vidyashankar, A.N. (2020): Robust estimation in controlled branching processes: Bayesian estimators via disparities, Bayesian Analysis, To Appear.
Li, L., Vidyashankar, A.N., Diao, G. and Ahmad, E. (2019): Robust inference after random projections via Hellinger distance for location-scale family, Entropy, 21, Issue 4, 1-40.
Hooker, G. and Vidyashankar, A.N. (2014): Bayesian model robustness via disparities, Test, 23, 556-584.
Cheng, An-lin, and Vidyashankar, A.N. (2006): Minimum Hellinger distance estimation for randomized play the winner design, Journal of Statistical Planning and Inference, 136,1875-2010.
Basawa, I.V. and Vidyashankar, A.N. (2003): Quasilikelihood estimation for branching processes with immigration, Journal of the Indian Statistical Association, 41, 157-172.
Sriram, T.N. and Vidyashankar, A.N. (2000): Minimum Hellinger distance estimation for supercritical Galton-Watson processes, Statistics and Probability Letters, 50, 331-342.
Basu, A., Sarkar, S., and Vidyashankar, A.N. (1997): Minimum negative disparity estimation in parametric models, Journal of Statistical Planning and Inference, 58, 349-370.

Sriram, T.N. and Vidyashankar, A.N. (2001): Sequential estimation for supercritical branching processes, Sequential Analysis, 20, pp. 263-277.
Sriram, T.N. and Vidyashankar, A.N. (2000): Sequential point estimation for branching processes-I, Subcritical Case, Sequential Analysis, 19, pp. 77-92.
Etemadi, N., Sriram, T.N., and Vidyashankar, A.N (1997): Lp Convergence of reciprocals of sample means with applications to sequential estimation in linear regression, Journal of Statistical Planning and Inference, 65, pp. 1-15.