Research Articles

Under Revision:

1. A. Biswas & A. Ramdas (2026+). Improving the adjusted Benjamini-Hochberg method using e-values in knockoff-assisted variable selection. 

arXiv: https://arxiv.org/abs/2602.11610.

2. P.Sarkar & A. Biswas (2026+). A model discrimination approach for testingindependence in paired data.  

Published:

23. A. Biswas & H. Parashar (2025). A note on dropout-time modeling under phase-wise variable stress fixed cohort setup with grouped data. Communications in Statistics: Case Studies, Data Analysis and Applications.

DOI: https://doi.org/10.1080/23737484.2025.2544842.

22. A. Nandi, A. Biswas, P. J. Hazarika & G. G. Hamedani (2025). A flexible count data model based on Bernoulli-Poisson-Geometric convolution. Communications in Statistics- Theory and Methods.

DOI: https://doi.org/10.1080/03610926.2025.2464081. 

21. P. Giri, A. Biswas & G. Chattopadhyay* (2024). Controlling the k-FWER by Adaptive Modified Bonferroni Methods under the Discrete Framework. International Journal of Statistical Sciences, 24 (2) [Invited paper for a special issue in the memory of Late Professor C R Rao].

DOI: https://doi.org/10.3329/ijss.v24i20.78218.

20. A. Biswas & G. Chattopadhyay (2024). Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm. Biometrical Journal, 66 (2).

DOI: https://doi.org/10.1002/bimj.202200204.

19. A Biswas, A Sinha (2024). Selective testing and its effect on false discovery rate controlling procedures under discrete framework. Communications in Statistics-Simulation and Computation.

DOI: https://doi.org/10.1080/03610918.2024.2412219

18. A. Biswas, S. Chakraborty & A. Nandi (2024). Modelling the time to dropout under phase-wise variable stress fixed cohort setup. Journal of Applied Statistics. 

DOI: https://doi.org/10.1080/02664763.2024.2392113

17. A. Nandi, S. Chakraborty & A. Biswas (2024). A new over-dispersed count model based on Poisson-Geometric convolution. Communications in Statistics: Simulation and Computation. 

DOI: https://doi.org/10.1080/03610918.2024.2329997

16. S. Amrin, A. Biswas, P. J. Hazarika, S. Chakraborty & A. Nandi (2024). On estimation of stress-strength reliability with zero-inflated Poisson distribution. Journal of Indian Society for Probability and Statistics. 

DOI: https://doi.org/10.1007/s41096-024-00195-8

15. A. Nandi, P. J. Hazarika, A. Biswas, G. G. Hamedani (2024). A new three parameter discrete distribution to model over-dispersed count data. Pakistan Journal of Statistics and Operation Research, 20 (2). 

DOI: https://doi.org/10.18187/pjsor.v20i2.4554

14. A. Nandi, A. Biswas, P. J. Hazarika & J. Das (2024). A new regression model for over-dispersed count responses based on Poisson and Geometric convolution. Journal of the Indian Society for Probability and Statistics. 

DOI: https://doi.org/10.1007/s41096-024-00199-4

13. A. Biswas, S. Chakraborty & I. Ghosh (2023). A novel method of generating distributions on the unit interval with applications. Communications in Statistics: Theory and Methods. 

DOI: https://doi.org/10.1080/03610926.2023.2280506

12. S. Chakraborty, S. H. Ong & A. Biswas, (2023). An extension of the geometric distribution with properties and applications.. Austrian Journal of Statistics, 52 (3). 

DOI: https://doi.org/10.17713/ajs.v52i3.1487

11. A. Biswas, S. Chakraborty & V. J. Baruah (2022). Estimation of the proportion of true null hypotheses under sparse dependence: Adaptive FDR controlling in microarray data. Statistical Methods in Medical Research, 31 (5). 

DOI: https://doi.org/10.1177/09622802221074164

10. A. Biswas & G. Chattopadhyay (2022). New results on adaptive false discovery rate control with p-value weighting. Statistical Papers, 64 (6). 

DOI: https://doi.org/10.1007/s00362-022-01369-x

9. A. Biswas, G. Chattopadhyay & A. Chatterjee (2022). Bias corrected estimators for proportion of true null hypotheses under exponential model: application of adaptive FDR-controlling in segmented failure data. Journal of Applied Statistics, 49 (14). 

DOI: https://doi.org/10.1080/02664763.2021.1957790

8. A. Biswas (2022). Estimating the proportion of true null hypotheses with application in microarray data. Communications in Statistics: Simulation and Computation, 51 (11). 

DOI: https://doi.org/10.1080/03610918.2020.1800036

7. A. Banerjee, S. Chakraborty & A. Biswas, (2022). Statistical Issues in Modelling Happiness Level of Immigrants: An Investigation with WHR, 2018. Thailand Statistician, 20 (1). 

DOI: https://ph02.tci-thaijo.org/index.php/thaistat/article/vi ew/245859/166753

6. S. Sinha, A. Hazarika, S. Johari, B. Neog, S. Rajkhowa & A. Biswas (2022). IMPDB: Indian medicinal phytochemical database curated for drug designing. Journal of Computational Biophysics and Chemistry, 21 (6). 

DOI: https://doi.org/10.1142/S2737416522500302

5. A. Biswas (2022). Model free bias reduction of Storey’s estimator for the proportion of true null hypotheses. Calcutta Statistical Association Bulletin, 74 (1). 

DOI: https://doi.org/10.1177/00080683221095155

4. A. Biswas, S. Chakraborty & M. Mukherjee (2021). On estimation of stress-strength reliability with log-Lindley distribution. Journal of Statistical Computation and Simulation, 91 (1). 

DOI: https://doi.org/10.1080/00949655.2020.1807549

3. M. Maiti, S. Chaudhuri & A. Biswas (2021). Activities of idol immersion leads to heavy metal contamination in river Hooghly in and around the city of Kolkata. Journal of the Indian Chemical Society, 98 (11). 

DOI: https://doi.org/10.1016/j.jics.2021.100223

2. A. Biswas & S. Chakraborty (2021). Stress-strength reliability for the unit-Lindley distribution with an application. Calcutta Statistical Association Bulletin, 73 (1). 

DOI: https://doi.org/10.1177/0008068321998111

1. A. Biswas (2020). Regarding paper “Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures” by Xiongzhi Chen, Rebecca W. Doerge and Joseph F. Heyse. Biometrical Journal, 62 (8). 

DOI: https://doi.org/10.1002/bimj.202000139

Book Chapters:

1. A. Biswas (2026). Parametric Statistical Inference. Fundamentals of Data Science - Vol. I., Springer. [To be published]

Doctoral Thesis

Title : On estimation of proportion of true null hypotheses in multiple testing problems. [Link to Thesis]

PG Dissertations Supervised

• At St. Xavier’s University, Kolkata

PG Dissertations:

2025

1. Statistical analysis of crimes against women in India — Ms. Dipanjana Kundu [Data Analyst at Digital Data Consultancy]

2. A comparative study of Statistical Learning Methods for classifying cancer subtypes using gene expression data — Mr. Dipmalya Roy [Data Analyst at Digital Data Consultancy]

         2024

1. Power function of permutation tests: A simulation approach — Mr. Anaranya Basu [Pursuing Ph.D. at Old Dominion University, USA] 

• At Dibrugarh University, Assam

PG Dissertations:

        2023

1. Modelling attrition of employees: A binary classification approach — Mr. Debasish Biswas

2. Modelling happiness level of immigrants: A binary classification approach with WHR, 2018 — Ms. Debashree Saikia

3. Important factors for students' placements: A statistical investigation — Ms. Kabyashree Saikia

2022

1. A study on effect of different factors on psychological well-being among the post-graduate students of Dibrugarh University — Ms. Snigdha Duarah

2. A study on effect of different factors on stress among the post-graduate students of Dibrugarh University — Mr. Deepjyoti Hazarika

2021

1. Stress-strength reliability with log-Lindley and beta distribution — Ms. Namrata Bhattacharjee [System Engineer, TCS Bangalore]

2. Stress-strength reliability with log-Lindley and Kumaraswamy distribution — Mr. Manab Jyoti Gogoi

3. Stress-strength reliability with Kumaraswamy and beta distributions — Ms. Nazneen Ara Begum

2020

1. Unit-Lindley distribution and its bivariate extension with an application — Mr. Monoj Borah 

2019

1. Statistical analysis of crime against women in India — Mr. Farhan Ahmed