Journals

Published/Accepted

Empirical likelihood confidence regions for ordered bivariate means. Statistical Papers (2026).

Naresh Garg (Arxiv)

Estimating location parameters of two exponential distributions with ordered scale parameters. Brazilian Journal of Probability and Statistics (2026)

Lakshmi Kanta Patra, Constantinos Petropoulos, Shrajal Bajpai and Naresh Garg (Arxiv)

Some Unified Results on Isotonic Regression Estimators of Order Restricted Parameters of a General Bivariate Location/Scale Model. Metrika (2025).

Naresh Garg and Neeraj Misra (Arxiv)

Isotonic Regression Estimators For Simultaneous Estimation of Order-Restricted Location/Scale Parameters of a Bivariate Distribution: A Unified Study. Ashis SenGupta Honour Volume: Directional and Multivariate Statistics (2025)

Naresh Garg and Neeraj Misra (Arxiv)

A Note On Simultaneous Estimation of Order Restricted Location Parameters of a General Bivariate Symmetric Distribution Under a General Loss Function. Statistics (2025).

Naresh Garg and Neeraj Misra (Arxiv)

On improved estimation of the larger location parameter. Statistical Papers (2024).

Naresh Garg, Lakshmi Kanta Patra, and Neeraj Misra (Arxiv)

A unified study for estimation of order restricted parameters of a general bivariate model under the generalized Pitman nearness criterion. Statistical Papers (2024).

Naresh Garg and Neeraj Misra (Arxiv)

Evaluation of PM 2.5 spatio-temporal variability and hotspot formation using low-cost sensors across urban-rural landscape in Lucknow, India. Atmospheric Environment (2024).

Sandeep Madhwal, Sachchida Nand Tripathi, Michael H. Bergin, Prakash Bhave, Benjamin de Foy, T V Ramesh Reddy, Sandeep Kumar Chaudhry, Vaishali Jain, Naresh Garg, Paresh Lalwani

Componentwise equivariant estimation of order restricted location and scale parameters in bivariate models: a unified study. Brazilian Journal of Probability and Statistics (2023).

Naresh Garg and Neeraj Misra (Arxiv)

Estimation of order restricted location/scale parameters of a general bivariate distribution under general loss function: some unified results. Japanese Journal of Statistics and Data Science (2022).

Naresh Garg and Neeraj Misra (Arxiv)

Preprint/Submitted

Jackknife Empirical Likelihood method for U Statistics based on Multivariate Samples and its Applications (2024).

Naresh Garg, Litty Mathew, Isha Dewan and Sudheesh Kumar Kattumannul (Submitted)

Empirical Likelihood Based Inference for a Divergence Measure Based on Survival Extropy (2025).

Naresh Garg, Isha Dewan and Sudheesh Kumar Kattumannul (Submitted)

Simultaneous Jackknife Empirical Likelihood Inference for Comparing Multiple Classifiers in Ordered Three-Class Problems (2026).

Naresh Garg

Conferences

Conference Proceedings / Published Abstracts

Shyam's Chromatography and TDM server in R shiny - an open-source program for TDM laboratories. Poster abstract published in the proceedings of the 18th World Congress of Basic and Clinical Pharmacology (WCP2018), Kyoto, Japan, 2018. DOI: 10.1254/jpssuppl.WCP2018.0_PO4-11-22.

Praveen Kumar M, Naresh Garg, Anand Srinivasan, Shruti Saha, Saurabh Sharma, Bikash Medhi

(https://utilityshiny.shinyapps.io/chromatographic_calculation_interpretation_database/)

Conference Presentations

Oral presentation, Open House, IIT Kanpur, India. “The Generalized Pitman Nearness Criterion for Estimation of order-restricted parameters,” Feb. 4, 2023.
Oral presentation, IISA Conference, IISc Bengaluru, India. “Shrinkage Estimators for Order restricted Parameters under the Generalized Pitman Nearness Criterion,” Dec. 26–30, 2022.
Oral presentation, 2nd International Cappadocia Scientific Research Congress, Turkey. “Component-wise Equivariant Estimation of Order Restricted Parameters in General Bivariate Models,” June 17–19, 2022.
Oral presentation, ISBIS Conference, Kochi, India. “Equivariant Estimation of Order Restricted Location Parameters,” Dec. 28–30, 2020.
Poster presentation, Institute Research Symposium, IIT Kanpur, India. “Equivariant Estimation of Order Restricted Location and Scale Parameters in General Bivariate Models: A Unified Study,” Jan. 7–8, 2023.