Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Chakravorty, Aritra"'
Data structures known as $k$-d trees have numerous applications in scientific computing, particularly in areas of modern statistics and data science such as range search in decision trees, clustering, nearest neighbors search, local regression, and s
Externí odkaz:
http://arxiv.org/abs/2201.08288
Designing scalable estimation algorithms is a core challenge in modern statistics. Here we introduce a framework to address this challenge based on parallel approximants, which yields estimators with provable properties that operate on the entirety o
Externí odkaz:
http://arxiv.org/abs/2112.15572
Autor:
Ghosh, Abhik, Chakravorty, Aritra
Copulas are mathematical objects that fully capture the dependence structure among random variables and hence, offer a great flexibility in building multivariate stochastic models. In statistics, a copula is used as a general way of formulating a mul
Externí odkaz:
http://arxiv.org/abs/1309.7503
Harnessing distributed computing environments to build scalable inference algorithms for very large data sets is a core challenge across the broad mathematical sciences. Here we provide a theoretical framework to do so along with fully implemented ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e18535041d975f338eff79e0acd3b2a
Autor:
Chakravorty, Aritra
In Divide & Recombine (D&R), data are divided into subsets, analytic methodsare applied to each subset independently, with no communication between processes;then the subset outputs for each method are recombined. For big data, this providesalmost al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1181ec1fb2db8e764525430cc7a8b0e6