Zobrazeno 1 - 10
of 148
pro vyhledávání: '"CHAKRABORTY, ABHISEK"'
Autor:
Mandal, Abhishek, Chakraborty, Abhisek
Survival regression is widely used to model time-to-events data, to explore how covariates may influence the occurrence of events. Modern datasets often encompass a vast number of covariates across many subjects, with only a subset of the covariates
Externí odkaz:
http://arxiv.org/abs/2409.10771
Autor:
Chakraborty, Abhisek, Datta, Saptati
Data dispersed across multiple files are commonly integrated through probabilistic linkage methods, where even minimal error rates in record matching can significantly contaminate subsequent statistical analyses. In regression problems, we examine sc
Externí odkaz:
http://arxiv.org/abs/2409.10678
The American Statistical Association (ASA) statement on statistical significance and P-values \cite{wasserstein2016asa} cautioned statisticians against making scientific decisions solely on the basis of traditional P-values. The statement delineated
Externí odkaz:
http://arxiv.org/abs/2402.13890
Autor:
Chakraborty, Abhisek, Datta, Saptati
Differential privacy has emerged as an significant cornerstone in the realm of scientific hypothesis testing utilizing confidential data. In reporting scientific discoveries, Bayesian tests are widely adopted since they effectively circumnavigate the
Externí odkaz:
http://arxiv.org/abs/2401.15502
We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, t
Externí odkaz:
http://arxiv.org/abs/2310.12447
Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification. However, when the functional data is observed over a large grid (say, of length $p$), Gaussian process cluster
Externí odkaz:
http://arxiv.org/abs/2309.07882
Autor:
Chakraborty, Abhisek
Advances in neuroscience have enabled researchers to measure the activities of large numbers of neurons simultaneously in behaving animals. We have access to the fluorescence of each of the neurons which provides a first-order approximation of the ne
Externí odkaz:
http://arxiv.org/abs/2307.10177
The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness. As clustering is one of the most commonly used unsupervised machine learning approaches, there has naturally been a proliferation of
Externí odkaz:
http://arxiv.org/abs/2305.17557
Flexible Bayesian models are typically constructed using limits of large parametric models with a multitude of parameters that are often uninterpretable. In this article, we offer a novel alternative by constructing an exponentially tilted empirical
Externí odkaz:
http://arxiv.org/abs/2303.10085
Publikováno v:
In iScience 20 September 2024 27(9)
