1 citations · 1 across the 8 of their papers we have counts for
8 papers
Eliciting core spatial association from spatial time series: a random matrix approach
Madhuchhanda Bhattacharjee, Arup Bose
Spatial time series (STS) data are fundamental to climate science, yet conventional approaches often conflate temporal co-evolution with genuine spatial dependence, obscuring subtl…
Spatio-temporal patterns of diurnal temperature: a random matrix approach I-case of India
Madhuchhanda Bhattacharjee, Arup Bose
We consider the spatio-temporal gridded daily diurnal temperature range (DTR) data across India during the 72-year period 1951--2022. We augment this data with information on the E…
Measuring spatial association and testing spatial independence based on short time course data
Divya Kappara, Arup Bose, Madhuchhanda Bhattacharjee
Spatial association measures for univariate static spatial data are widely used. When the data is in the form of a collection of spatial vectors with the same temporal domain of in…
Assessing bivariate independence: Revisiting Bergsma's covariance
Divya Kappara, Arup Bose, Madhuchhanda Bhattacharjee
Bergsma (2006) proposed a covariance (X,Y) between random variables X and Y. He derived their asymptotic distributions under the null hypothesis of independence between X and Y.…
Modelling COVID-19-III: endemic spread in India
Madhuchhanda Bhattacharjee, Arup Bose
A disease in a given population is termed endemic when it exhibits a steady prevalence. We address the pertinent question as to what extent COVID-19 has turned endemic in India. Th…
Modelling COVID-19 -- I A dynamic SIR(D) with application to Indian data
Madhuchhanda Bhattacharjee, Arup Bose
We propose an epidemiological model using an adaptive dynamic three compartment (with four states) SIR(D) model. Our approach is similar to non-parametric curve fitting in spirit a…