Journal Articles:
Journal Articles:
Biswas, S., and Banerjee, B,: An Efficient Sampling from Circular Distributions and its Extension to Toroidal Distributions. Communications in Statistics-Simulation and Computation (2026). Link
Biswas, S.: Some stochastic comparison results of the highest and lowest order statistics of independent exponentiated Gumbel type-II distribution. Mathematica Slovaca (2026). Link
Banerjee, B., Biswas, S.: Intrinsic geometry-inspired dependent toroidal distribution: application to regression model for astigmatism data. Computational Statistics (2026). Link
Biswas, S., Banerjee, B.: Semi-parametric least-area linear-circular regression through M\"{o}bius transformation, Statistical Analysis and Data Mining: The American Statistical Association (ASA) Data Science Journal (2025). Link
Biswas, S., & Gupta, N.: Stochastic Ordering Inequalities in Parallel and Series Systems with Gumbel Distributed Components. Calcutta Statistical Association Bulletin (2022). Link
Preprints:
Banerjee, B., Biswas, S. and Prusti, D. : Hyperbolic statistical inference for Treatment Effects with Circular biomarker of astigmatism. (In revision with the Statistics in Medicine)
Biswas, S., Banerjee, B, and Laha, A.K.: A geometric approach in non-parametric Changepoint detection in circular data. (Communicated)
Biswas, S., Banerjee, B,: A Semi-Parametric Torus-to-Torus Regression Model with Geometric Loss: Application to Cyclone Data. (In revision with the Journal of the Royal Statistical Society: Series C )
Biswas, S., Banerjee, B, and Laha, A.K.: Intrinsic Geometry-Based Angular Covariance: A Novel Framework for Nonparametric Changepoint Detection in Meteorological Data. (In revision with the Annals of Applied Statistics (AOAS))
Biswas, S., and Banerjee, B,: Geometry-Driven Non-Parametric Test for Changepoint Detection in Angular Representation of Cryptocurrency Timestamps at Extreme-Values. (Communicated)
Banerjee, B, and Biswas, S.: Sampling from the surface of a curved torus: A new genesis.
Ongoing:
Deep Circular Clustering Models for Genomic and Proteomic Analysis.
Testing the Structural Differences in Genomic Data via Persistent Homology.
Thesis: