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pro vyhledávání: '"Mukerjee, Rahul"'
Autor:
Mukerjee, Rahul
Augmented block designs for unreplicated test treatments are investigated under the A- and MV-criteria with respect to control versus control, test versus test and control versus test comparisons. We derive design-independent lower bounds on these cr
Externí odkaz:
http://arxiv.org/abs/2310.18692
Autor:
Mukerjee, Rahul
The multi-scale mixed finite element method (MsMFEM) discussed in this work uses a two-scale approach, where the solutions to independent local flow problems on the fine grid capture the fine-scale variations of the reservoir model, while the coarse
Externí odkaz:
http://hdl.handle.net/1969.1/ETD-TAMU-2803
Autor:
Mukerjee, Rahul
Let a stick be broken at random at n-1 points to form n pieces. We consider three problems on forming k-gons with k out of these n pieces, and show how a statistical approach, through a linear transformation of variables, yields simple solutions that
Externí odkaz:
http://arxiv.org/abs/2207.07879
Autor:
Mukerjee, Rahul, Elvira, Víctor
Multiple importance sampling (MIS) is an increasingly used methodology where several proposal densities are used to approximate integrals, generally involving target probability density functions. The use of several proposals allows for a large varie
Externí odkaz:
http://arxiv.org/abs/2207.04187
Improving upon the effective sample size based on Godambe information for block likelihood inference
Autor:
Mukerjee, Rahul
We consider the effective sample size, based on Godambe information, for block likelihood inference which is an attractive and computationally feasible alternative to full likelihood inference for large correlated datasets. With reference to a Gaussi
Externí odkaz:
http://arxiv.org/abs/2112.09840
Autor:
Mukerjee, Rahul, Dasgupta, Tirthankar
Split-plot designs find wide applicability in multifactor experiments with randomization restrictions. Practical considerations often warrant the use of unbalanced designs. This paper investigates randomization based causal inference in split-plot de
Externí odkaz:
http://arxiv.org/abs/1906.08420
Autor:
Mukerjee, Rahul, Dasgupta, Tirthankar
Publikováno v:
Statistica Sinica, 2022 Jan 01. 32, 591-612.
Externí odkaz:
https://www.jstor.org/stable/27108174
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Strip-plot designs are very useful when the treatments have a factorial structure and the factors levels are hard-to-change. We develop a randomization-based theory of causal inference from such designs in a potential outcomes framework. For any trea
Externí odkaz:
http://arxiv.org/abs/1805.06663